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PSPP is a program for statistical analysis of sampled data. It is a Free replacement for the proprietary program SPSS, and appears very similar to it with a few exceptions.
The most important of these exceptions are, that there are no “time bombs”; your copy of PSPP will not “expire” or deliberately stop working in the future. Neither are there any artificial limits on the number of cases or variables which you can use. There are no additional packages to purchase in order to get “advanced” functions; all functionality that PSPP currently supports is in the core package.
PSPP can perform descriptive statistics, T-tests, linear regression and non-parametric tests. Its backend is designed to perform its analyses as fast as possible, regardless of the size of the input data. You can use PSPP with its graphical interface or the more traditional syntax commands.
A brief list of some of the features of PSPP follows:
- Supports over 1 billion cases.
- Supports over 1 billion variables.
- Syntax and data files are compatible with SPSS.
- Choice of terminal or graphical user interface.
- Choice of text, postscript or html output formats.
- Inter-operates with Gnumeric, OpenOffice.Org and other free software.
- Easy data import from spreadsheets, text files and database sources.
- User interface translated to multiple languages (details).
- Fast statistical procedures, even on very large data sets.
- No license fees.
- No expiration period.
- No unethical “end user license agreements”.
- Fully indexed user manual.
- Free Software; licensed under GPLv3 or later.
- Cross platform; Runs on many different computers and many different operating systems.
PSPP is particularly aimed at statisticians, social scientists and students requiring fast convenient analysis of sampled data.
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PSPP - SPSS predictive analytics software, you can predict with confidence what will happen next so that you can make smarter decisions, solve problems and improve outcomes
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- SPSS - Wikipedia, the free encyclopedia
SPSS v.19 x86 running on Windows 7 x86
|Stable release||21.0 August 21, 2012|
|Operating system||Windows, zLinux, Linux / UNIX & Mac|
|Type||Statistical analysis, Data Mining, Text Analytics, Data Collection, Collaboration & Deployment|
SPSS Statistics is a software package used for statistical analysis. It is now officially named "IBM SPSS Statistics". Companion products in the same family are used for survey authoring and deployment (IBM SPSS Data Collection), data mining (IBM SPSS Modeler), text analytics, and collaboration and deployment (batch and automated scoring services).
SPSS (originally, Statistical Package for the Social Sciences, later modified to read Statistical Product and Service Solutions) was released in its first version in 1968 after being developed by Norman H. Nie, Dale H. Bent and C. Hadlai Hull. SPSS is among the most widely used programs for statistical analysis in social science. It is used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations and others. The original SPSS manual (Nie, Bent & Hull, 1970) has been described as one of "sociology's most influential books". In addition to statistical analysis, data management (case selection, file reshaping, creating derived data) and data documentation (a metadata dictionary is stored in the datafile) are features of the base software. SPSS was released in its second version in 1972 and its company name is INDUS Nomi.
Statistics included in the base software:
- Descriptive statistics: Cross tabulation, Frequencies, Descriptives, Explore, Descriptive Ratio Statistics
- Bivariate statistics: Means, t-test, ANOVA, Correlation (bivariate, partial, distances), Nonparametric tests
- Prediction for numerical outcomes: Linear regression
- Prediction for identifying groups: Factor analysis, cluster analysis (two-step, K-means, hierarchical), Discriminant
The many features of SPSS are accessible via pull-down menus or can be programmed with a proprietary 4GL command syntax language. Command syntax programming has the benefits of reproducibility, simplifying repetitive tasks, and handling complex data manipulations and analyses. Additionally, some complex applications can only be programmed in syntax and are not accessible through the menu structure. The pull-down menu interface also generates command syntax; this can be displayed in the output, although the default settings have to be changed to make the syntax visible to the user. They can also be pasted into a syntax file using the "paste" button present in each menu. Programs can be run interactively or unattended, using the supplied Production Job Facility. Additionally a "macro" language can be used to write command language subroutines and a Python programmability extension can access the information in the data dictionary and data and dynamically build command syntax programs. The Python programmability extension, introduced in SPSS 14, replaced the less functional SAX Basic "scripts" for most purposes, although SaxBasic remains available. In addition, the Python extension allows SPSS to run any of the statistics in the free software package R. From version 14 onwards SPSS can be driven externally by a Python or a VB.NET program using supplied "plug-ins".
SPSS places constraints on internal file structure, data types, data processing and matching files, which together considerably simplify programming. SPSS datasets have a 2-dimensional table structure where the rows typically represent cases (such as individuals or households) and the columns represent measurements (such as age, sex or household income). Only 2 data types are defined: numeric and text (or "string"). All data processing occurs sequentially case-by-case through the file. Files can be matched one-to-one and one-to-many, but not many-to-many.
The graphical user interface has two views which can be toggled by clicking on one of the two tabs in the bottom left of the SPSS window. The 'Data View' shows a spreadsheet view of the cases (rows) and variables (columns). Unlike spreadsheets, the data cells can only contain numbers or text and formulas cannot be stored in these cells. The 'Variable View' displays the metadata dictionary where each row represents a variable and shows the variable name, variable label, value label(s), print width, measurement type and a variety of other characteristics. Cells in both views can be manually edited, defining the file structure and allowing data entry without using command syntax. This may be sufficient for small datasets. Larger datasets such as statistical surveys are more often created in data entry software, or entered during computer-assisted personal interviewing, by scanning and using optical character recognition and optical mark recognition software, or by direct capture from online questionnaires. These datasets are then read into SPSS.
SPSS can read and write data from ASCII text files (including hierarchical files), other statistics packages, spreadsheets and databases. SPSS can read and write to external relational database tables via ODBC and SQL.
Statistical output is to a proprietary file format (*.spv file, supporting pivot tables) for which, in addition to the in-package viewer, a stand-alone reader can be downloaded. The proprietary output can be exported to text or Microsoft Word, PDF, Excel, and other formats. Alternatively, output can be captured as data (using the OMS command), as text, tab-delimited text, PDF, XLS, HTML, XML, SPSS dataset or a variety of graphic image formats (JPEG, PNG, BMP and EMF).
SPSS Server is a version of SPSS with a client/server architecture. It had some features not available in the desktop version, such as scoring functions (Scoring functions are included in the desktop version from version 19).
Early versions of SPSS were designed for batch processing on mainframes, including for example IBM and ICL versions, originally using punched cards for input. A processing run read a command file of SPSS commands and either a raw input file of fixed format data with a single record type, or a 'getfile' of data saved by a previous run. To save precious computer time an 'edit' run could be done to check command syntax without analysing the data. From version 10 (SPSS-X) in 1983, data files could contain multiple record types.
SPSS versions 16.0 and later run under Windows, Mac, and Linux. The graphical user interface is written in Java. The Mac OS version is provided as a Universal binary, making it fully compatible with both PowerPC and Intel-based Mac hardware.
Prior to SPSS 16.0, different versions of SPSS were available for Windows, Mac OS X and Unix. The Windows version was updated more frequently, and had more features, than the versions for other operating systems.
SPSS version 13.0 for Mac OS X was not compatible with Intel-based Macintosh computers, due to the Rosetta emulation software causing errors in calculations. SPSS 15.0 for Windows needed a downloadable hotfix to be installed in order to be compatible with Windows Vista.
Between 2009 and 2010, SPSS Inc. referred to its primary product lines under the PASW (Predictive Analytics SoftWare) banner, rather than referring to the both the company and its products as "SPSS".
The company announced on July 28, 2009 that it was being acquired by IBM for US$1.2 billion. As of January 2010, it became "SPSS: An IBM Company". Complete transfer of business to IBM was done by October 1, 2010. By that date, SPSS: An IBM Company ceased to exist. IBM SPSS is now fully integrated into the IBM Corporation, and is one of the brands under IBM Software Group's Business Analytics Portfolio, together with IBM Cognos.
Add-on modules provide additional capabilities. The available modules are:
- SPSS Programmability Extension (added in version 14). Allows Python, R, and .NET programming control of SPSS.
- SPSS Data Preparation (added in version 14). Allows programming of logical checks and reporting of suspicious values.
- SPSS Regression - Logistic regression, ordinal regression, multinomial logistic regression, and mixed models.
- SPSS Advanced Models - Multivariate GLM and repeated measures ANOVA (removed from base system in version 14).
- SPSS Decision Trees. Creates classification and decision trees for identifying groups and predicting behaviour.
- SPSS Custom Tables. Allows user-defined control of output for reports.
- SPSS Exact Tests. Allows statistical testing on small samples.
- SPSS Categories
- SPSS Forecasting
- SPSS Conjoint
- SPSS Missing Values. Simple regression-based imputation.
- SPSS Complex Samples (added in Version 12). Adjusts for stratification and clustering and other sample selection biases.
- AMOS (Analysis of Moment Structures) - add-on which allows modeling of structural equation and covariance structures, path analysis, and has the more basic capabilities such as linear regression analysis, ANOVA and ANCOVA
- SPSSx release 2 - 1983
- SPSS 5.0 - December 1993
- SPSS 6.1 - February 1995
- SPSS 7.5 - January 1997
- SPSS 8.0 - 1998
- SPSS 9.0 - March 1999
- SPSS 10.0.5 - December 1999
- SPSS 10.0.7 - July 2000
- SPSS 10.1.4 - January 2002
- SPSS 11.0.1 - April 2002
- SPSS 11.5.1 - April 2003
- SPSS 12.0.1 - July 2004
- SPSS 13.0.1 - March 2005
- SPSS 14.0.1 - January 2006
- SPSS 15.0.1 - November 2006
- SPSS 16.0.1 - December 2007
- SPSS 16.0.2 - April 2008
- SPSS Statistics 17.0.1 - December 2008
- PASW Statistics 17.0.3 - September 2009
- PASW Statistics 18.0 - August 2009
- PASW Statistics 18.0.1 - December 2009
- PASW Statistics 18.0.2 - April 2010
- PASW Statistics 18.0.3 - September 2010
- IBM SPSS Statistics 19.0 - August 2010
- IBM SPSS Statistics 19.0.1 - December 2010
- IBM SPSS Statistics 20.0 - August 2011
- IBM SPSS Statistics 20.0.1 - March 2012
- IBM SPSS Statistics 21.0 - August 2012
Version comparison 21 to 7
(From the orignal SPSS website - saved as release occurred. Italicized text was not included in SPSS list.)
SPSS Version 21.0 Added
- Monte Carlo simulation techniques to build better models and assess risk when inputs are uncertain.
- Compatibility - The ability for IBM SPSS to leverage data through integration with technologies and tools like Cognos BI.
- Enhancements to client/server technology to improve performance and give organizations more flexibility when dealing with large scales of data.
- Improved productivity with improvements to existing technology allows existing users to increase production through software enhancements.
SPSS Version 20.0 Added
- Map visualization capabilities, including pre-built map templates and support for ESRI files in IBM SPSS Statistics Base
- Faster tables: Generate fully interactive and editable output tables up to five times faster
- Generalized linear mixed models (GLMM) can be run with a wide variety of hierarchical data (including ordinal values) in IBM SPSS Advanced Statistics
- Run server jobs while disconnected from the network in IBM SPSS Statistics Server
- Temporary file compression during sort procedure with SPSS Statistics Server
- Non-graphical method to specify models in IBM SPSS Amos
SPSS Version 19.0 Added
- Generalized linear mixed models (GLMM)
- Improvements to crosstabs
- Automatic linear models, with improved model accuracy and stability with boosting and bagging
- Updates to IBM SPSS Direct Marketing, including smart output and improvements to control package testing
- UIs for scoring, allowing customers to more easily score new data with pre-built models
- Improvements to portal: Ability to run IBM SPSS Statistics in a portal, combined with IBM SPSS Collaboration and Deployment Services
- Ability to connect to Salesforce.com
- Extended IBM SPSS Exact Tests platform support to Mac and Linux
- Import/Export Microsoft® Excel® 2010 data
SPSS Version 18.0 Added
- All modules can run independently, IBM SPSS Statistics Base is no longer required
- Prepare data in a single step using automated data preparation feature
- New nonparametric tests in SPSS Statistics Base
- Post computed categories in IBM SPSS Custom Tables
- Ability to view significance tests in the main results table in SPSS Custom Tables
- Interactive model viewer on two-step cluster analysis and automated data preparation procedures
- Improved display of large pivot tables
- New product: IBM SPSS Statistics Developer and new modules: IBM SPSS Direct Marketing and IBM SPSS Bootstrapping
SPSS Version 17.0 Added
- Switch user interface language
- Mac OS X and Linux platforms can connect clients to SPSS Statistics Server
- Updated plug-ins for Python, .NET, and R
- Support for graphic packages written in R
- Create user defined interfaces for existing procedures and user-defined procedures with Custom Dialog Builder
- Call front-end Python scripts or scripting APIs explicitly from within back-end Python programs
- Support for Predictive Enterprise View, a common data interface that can defined once and used by all SPSS Inc. analytic tools
- Administrative enhancements in SPSS Statistics Server, including optimized multithreading, virtualization support, and a “file in use” message to reduce errors in data created by more than one person writing to an SPSS Statistics file at the same time
- Read access to SPSS Statistics data files as an ODBC/JDBC data source, allowing these files to be read using SQL
- Codebook procedure to automatically describe the dataset
- Improved Syntax Editor with features to make it easier to create, test, and deploy syntax jobs
- Spell-checking of long text strings
- SPSS EZ RFM add-on module
- Multiple imputation of missing data in SPSS Missing Values add-on module
- Regularization methods: Ridge regression, the Lasso, Elastic Net all in the SPSS Categories add-on module
- Model selection methods: 632(+), bootstrap, cross validation(CV) all in the SPSS Categories add-on module
- Nearest Neighbor analysis in SPSS Statistics Base
- Median transformations function in COMPUTE procedure
- Option to use aggressive versus conservative rounding in COMPUTE procedure
- Create new variables that contain the values of existing variables from preceding or subsequent cases
- Graphboard integration, enabling users of SPSS Statistics products to deploy new or customer graph templates created in the new SPSS Viz Designer stand-alone module
- Wrapping and shrinking of wide tables in Word and PowerPoint-Smartreader feature to allow the viewing and pivoting of SPSS Statistics output
SPSS Version 16.0 Added
- Mac and Linux versions of SPSS
- Several multithreaded procedures for improved performance and scalability
- In the Data Editor: ability to customize variable view
- In the Data Editor: spell checking for value labels and variable labels
- In the Data Editor: ability to sort by variable name, type, format, etc.
- Unicode support
- Import/export Excel 2007 data
- Syntax to change string length and basic data type of existing variables
- Creation of value labels and missing values on strings of any length
- Ability to set a permanent default working directory
- SPSS Neural Networks add-on module
- Complex Samples Cox Regression added to SPSS Complex Samples
- Latent Class Analysis in Amos
- Partial Least Squares regression
- Support for R algorithms
- Find and Replace feature in the Output Viewer
SPSS Version 15.0 Added
- SPSS Adapter for SPSS Predictive Enterprise Services
- Updated PMML to include transformations
- Single administration utility for SPSS Server, Clementine Server, and SPSS Predictive Enterprise Services platform
- Stripe temporary files over multiple disks for increased performance (in SPSS Server)
- Export to Database Wizard
- Custom Attributes for user-defined meta data in the SPSS Data Editor
- Export to Dimensions Data Model
- Optimal Binning (in SPSS Data Preparation™; this add-on module was previously called SPSS Data Validation)
- Subset variable views
- Python® programming language included on the SPSS CD (in SPSS Programmability Extension)
- Ability to create first-class, user-defined procedures (in SPSS Programmability Extension)
- Syntax control of output files (in SPSS Programmability Extension)
- Generalized linear models (in SPSS Advanced Models)
- Generalized estimating equations (in SPSS Advanced Models)
- Ordinal regression to model ordinal outcomes
- Complex samples ordinal regression (in SPSS Complex Samples)
- Estimation and imputation of ordered-categorical and censored data (in Amos 7.0 structural equation modeling software)
- Dual-Y axis and overlay charts
- Enhanced process control charts
- Export output to PDF (in SPSS Base
- Results Coach™ no longer available
SPSS Version 14.0 Added
- Data-free client (in SPSS Server)
- SPSS Adapter for Predictive Enterprise Services (available in SPSS 14.0.1)
- User interface (UI) for scoring (in SPSS Server)
- Predictor selection algorithm (in SPSS Server)
- Naïve Bayes algorithm (in SPSS Server)
- Clone dataset command
- Open multiple datasets within a single SPSS session
- Long value labels (up to 120 bytes)
- Support for Dimensions Data Model™ and traditional market research software
- OLE DB data access
- Control the flow of your syntax jobs using an external programming language (through SPSS Programmability Extensions™)
- Validate Data procedure (in SPSS Data Validation™)
- Anomaly detection for multivariate outliers (in SPSS Data Validation)
- Enhanced SPSS Trends™ add-on module with Expert Modeler
- Bayesian estimation—MCMC algorithm (in Amos™ 6.0 structural equation modeling software)
- Data imputation, including multiple imputation (in Amos 6.0 structural equation modeling software)
- Run significance tests on multiple response variables (in SPSS Tables™)
- Exclude categories used in subtotal calculations from significance tests (in SPSS Tables)
- Preference scaling (PREFSCAL) procedure (in SPSS Categories™)
- Chart Builder user interface for graphics
- Support for SPSS Inc.'s Graphics Production Language (GPL)
- 2-D line charts (both axes can be scale axes) and charts for multiple response sets
- Network license reservations and priority settings
- Network commuter license
- License manager redundancy
SPSS Version 13.0 Added
- In-database data preparation (sort and aggregate) to improve performance (SPSS Server only)
- Create and save PMML models for scoring data at a later time (SPSS Server only)
- Date and Time Wizard
- Very long text strings (up to 32,767 bytes)
- Interactive interface for the Output Management System (OMS)
- Complex samples general linear model (GLM) (in SPSS Complex Samples add-on module)
- Complex samples logistic regression (in SPSS Complex Samples add-on module)
- SPSS Classification Trees™ add-on module
- Multiple correspondence analysis (in SPSS Categories™ add-on module)
- Population pyramids (also called mirror charts or dual charts), 3-D bar charts, and dot charts (also called dot density charts)
- Additional chart display features/options, including paneled charts and error bars on categorical charts
- Export output to Microsoft® PowerPoint®
SPSS Version 12.0 Added
- Support for Open SSL
- Presentation graphics system
- Identify Duplicate Cases tool
- Long variable names (up to 64 bytes)
- Visual Bander to easily create bands (e.g., break income into income "bands" of 10,000)
- Output Management System (turn pivot table output, such as SPSS data files, XML, and HTML into data/input)
- SPSS Complex Samples™ add-on module
- Stepwise Multinomial Regression (in SPSS Regression Models™ add-on module)
- SPSS Manuals on CD, featuring manuals in PDF format for SPSS Base and all add-on modules
- 64-bit version of SPSS Server (coming soon)
- Multithreaded ODBC (in SPSS Server)
SPSS Version 11.5 Added
- Export data to recent versions of Excel® and SAS®
- Enhanced SPSS Tables™ add-on module
- Export output to Excel
- Export output to Word
- Switch output language
- Data prep tools to get your data ready for analysis
SPSS Version 11.0 Added
- Support for communication between SPSS for Windows and SPSS Server
- Restructure Data Wizard
- Descriptive ratio statistics
- Improved scalability and performance of Multinomial Logistic Regression (completed in version 10.1 in SPSS Regression Models module
- Improved scalability and performance of Proximities and Hierarchical Cluster Analysis (completed in SPSS 10.1)
- Linear mixed models (a.k.a. Hierarchical Linear Models; in SPSS Advanced Models™ module)
- Percent change in OLAP Cubes
- Online case studies
SPSS Version 10.0 Added
- Each module on the desktop has a related module server available (for SPSS Server)
- Conversion-free/copy-free data access in SQL DBMS (in SPSS Server)
- Data stays on server, which is where procedures run (in SPSS Server)
- Download data only if you want to (in SPSS Server)
- SPSS client can be used with all licensed SPSS Servers
- Actively manage temp file consolidation
- Direct Excel interface
- Large file capability
- New flexible Data Editor
- Read recent SAS files
- Ability to run multiple SPSS sessions at one time and switch sessions
- CATPCA and PROXSCAL (in SPSS Categories™ add-on module)
- PLUM (in SPSS Advanced Models add-on module)
- SPSS Maps™ add-on module XML file export for model deployment with SmartScore™
SPSS Version 9.0 Added
- Text Wizard
- Multinomial logistic regression (in SPSS Regression Models add-on module)
- Reliability and ALSCAL Multidimensional Scaling and matrix operations (in SPSS Base)
- ROC (Receiver-Operating Characteristic) analysis
- Customization options: secondary axis, reference lines, hiding and reordering categories, specifying min/max values from dialog box, spikes
- IGRAPH Scripting
- Table to graph
SPSS Version 8.0 Added
- Long variable labels in dialogs
- Chart Looks
- Chart rotation and lighting controls
- Chart types: ribbon, error bar on bar chart, plotted pie, true 3-D bar and true 3-D pie
- Interactive chart types: stacked bar, area and multiple response
- IGRAPH (Interactive graphs)
- Draft Viewer/Text output and control
- Layered Reports/OLAP cubes
- Results Coach™
SPSS Version 7.x Added
- 256 character-length labels
- Customizable toolbar
- Database Wizard
- Cluster analysis (in SPSS Base)
- Discriminant analysis (in SPSS Base)
- Factor analysis (in SPSS Base)
- GLM (in SPSS Advanced Models add-on module)
- Varcomp (in Advanced Models add-on module)
- HTML output
- Output Navigator/Viewer
- Pivot tables/Report cubes
- Output scripting programming in BASIC
- Statistical Coach™
- "What's This" (context sensitive help)
|Wikiversity has learning materials about SPSS|
- SPSS Modeler
- Comparison of statistical packages
- PSPP – an open source alternative
- gretl – an open source alternative that can import SPSS data files
- R Commander - an open source alternative based on the R programming language
- Rkward - an open source alternative based on the R programming language, designed for and integrates with the KDE desktop environment
- Data mining
- Data processing
- Argyrous, G. Statistics for Research: With a Guide to SPSS, SAGE, London, ISBN 1-4129-1948-7
- Levesque, R. SPSS Programming and Data Management: A Guide for SPSS and SAS Users, Fourth Edition (2007), SPSS Inc., Chicago Ill. PDF ISBN 1-56827-390-8
- SPSS 15.0 Command Syntax Reference 2006, SPSS Inc., Chicago Ill.
- Wellman, B. "Doing It Ourselves: The SPSS Manual as Sociology's Most Influential Recent Book."pp. 71–78 in Required Reading: Sociology's Most Influential Books, edited by Dan Clawson. Amherst: University of Massachusetts Press, 1998.
- Official website
- Raynald Levesque's SPSS Tools - library of worked solutions for SPSS programmers (FAQ, command syntax; macros; scripts; python)
- Archives of SPSSX-L Discussion - SPSS Listserv active since 1996. Discusses programming, statistics and analysis
- UCLA ATS Resources to help you learn SPSS - Resources for learning SPSS
- UCLA ATS Technical Reports - Report 1 compares Stata, SAS and SPSS against R (R is a language and environment for statistical computing and graphics).
- Using SPSS For Data Analysis - SPSS Tutorial from Harvard
- SPSS Community - Support for developers of applications using SPSS products, including materials and examples of the Python and R programmability features
Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), a field at the intersection of computer science and statistics, is the process that attempts to discover patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
The term is a buzzword, and is frequently misused to mean any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) but is also generalized to any kind of computer decision support system, including artificial intelligence, machine learning, and business intelligence. In the proper use of the word, the key term is discovery, commonly defined as "detecting something new". Even the popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" – or when referring to actual methods, artificial intelligence and machine learning – are more appropriate.
The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually involves using database techniques such as spatial indexes. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps.
The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
In the 1960s, statisticians used terms like "Data Fishing" or "Data Dredging" to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term "Data Mining" appeared around 1990 in the database community. At the beginning of the century, there was a phrase "database mining"™, trademarked by HNC, a San Diego-based company (now merged into FICO), to pitch their Data Mining Workstation; researchers consequently turned to "data mining". Other terms used include Data Archaeology, Information Harvesting, Information Discovery, Knowledge Extraction, etc. Gregory Piatetsky-Shapiro coined the term "Knowledge Discovery in Databases" for the first workshop on the same topic (1989) and this term became more popular in AI and Machine Learning Community. However, the term data mining became more popular in the business and press communities. Currently, Data Mining and Knowledge Discovery are used interchangeably..
The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever larger data sets.
Research and evolution
The premier professional body in the field is the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). Since 1989 they have hosted an annual international conference and published its proceedings, and since 1999 have published a biannual academic journal titled "SIGKDD Explorations".
Computer science conferences on data mining include:
- CIKM Conference – ACM Conference on Information and Knowledge Management
- DMIN Conference – International Conference on Data Mining
- DMKD Conference – Research Issues on Data Mining and Knowledge Discovery
- ECDM Conference – European Conference on Data Mining
- ECML-PKDD Conference – European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
- EDM Conference – International Conference on Educational Data Mining
- ICDM Conference – IEEE International Conference on Data Mining
- KDD Conference – ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- MLDM Conference – Machine Learning and Data Mining in Pattern Recognition
- PAKDD Conference – The annual Pacific-Asia Conference on Knowledge Discovery and Data Mining
- PAW Conference – Predictive Analytics World
- SDM Conference – SIAM International Conference on Data Mining (SIAM)
- SSTD Symposium – Symposium on Spatial and Temporal Databases
- WSDM Conference – ACM Conference on Web Search and Data Mining
The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:
- (1) Selection
- (2) Pre-processing
- (3) Transformation
- (4) Data Mining
- (5) Interpretation/Evaluation.
It exists, however, in many variations on this theme, such as the Cross Industry Standard Process for Data Mining (CRISP-DM) which defines six phases:
- (1) Business Understanding
- (2) Data Understanding
- (3) Data Preparation
- (4) Modeling
- (5) Evaluation
- (6) Deployment
or a simplified process such as (1) pre-processing, (2) data mining, and (3) results validation.
Polls conducted in 2002, 2004, and 2007 show that the CRISP-DM methodology is the leading methodology used by data miners. The only other data mining standard named in these polls was SEMMA. However, 3-4 times as many people reported using CRISP-DM. Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.
Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data warehouse. Pre- processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.
Data mining involves six common classes of tasks:
- Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors and require further investigation.
- Association rule learning (Dependency modeling) – Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
- Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
- Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".
- Regression – Attempts to find a function which models the data with the least error.
- Summarization – providing a more compact representation of the data set, including visualization and report generation.
|This section is missing information about non-classification tasks in data mining, it only covers machine learning. (September 2011)|
The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the data mining algorithms are necessarily valid. It is common for the data mining algorithms to find patterns in the training set which are not present in the general data set. This is called overfitting. To overcome this, the evaluation uses a test set of data on which the data mining algorithm was not trained. The learned patterns are applied to this test set and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish "spam" from "legitimate" emails would be trained on a training set of sample e-mails. Once trained, the learned patterns would be applied to the test set of e-mails on which it had not been trained. The accuracy of the patterns can then be measured from how many e-mails they correctly classify. A number of statistical methods may be used to evaluate the algorithm, such as ROC curves.
If the learned patterns do not meet the desired standards, then it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns do meet the desired standards, then the final step is to interpret the learned patterns and turn them into knowledge.
There have been some efforts to define standards for the data mining process, for example the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006, but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.
For exchanging the extracted models – in particular for use in predictive analytics – the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover (for example) subspace clustering have been proposed independently of the DMG.
Since the early 1960s, with the availability of oracles for certain combinatorial games, also called tablebases (e.g. for 3x3-chess) with any beginning configuration, small-board dots-and-boxes, small-board-hex, and certain endgames in chess, dots-and-boxes, and hex; a new area for data mining has been opened. This is the extraction of human-usable strategies from these oracles. Current pattern recognition approaches do not seem to fully acquire the high level of abstraction required to be applied successfully. Instead, extensive experimentation with the tablebases – combined with an intensive study of tablebase-answers to well designed problems, and with knowledge of prior art (i.e. pre-tablebase knowledge) – is used to yield insightful patterns. Berlekamp (in dots-and-boxes, etc.) and John Nunn (in chess endgames) are notable examples of researchers doing this work, though they were not – and are not – involved in tablebase generation.
Data mining in customer relationship management applications can contribute significantly to the bottom line. Rather than randomly contacting a prospect or customer through a call center or sending mail, a company can concentrate its efforts on prospects that are predicted to have a high likelihood of responding to an offer. More sophisticated methods may be used to optimize resources across campaigns so that one may predict to which channel and to which offer an individual is most likely to respond (across all potential offers). Additionally, sophisticated applications could be used to automate mailing. Once the results from data mining (potential prospect/customer and channel/offer) are determined, this "sophisticated application" can either automatically send an e-mail or a regular mail. Finally, in cases where many people will take an action without an offer, "uplift modeling" can be used to determine which people have the greatest increase in response if given an offer. Data clustering can also be used to automatically discover the segments or groups within a customer data set.
Businesses employing data mining may see a return on investment, but also they recognize that the number of predictive models can quickly become very large. Rather than using one model to predict how many customers will churn, a business could build a separate model for each region and customer type. Then, instead of sending an offer to all people that are likely to churn, it may only want to send offers to loyal customers. Finally, the business may want to determine which customers are going to be profitable over a certain window in time, and only send the offers to those that are likely to be profitable. In order to maintain this quantity of models, they need to manage model versions and move on to automated data mining.
Data mining can also be helpful to human resources (HR) departments in identifying the characteristics of their most successful employees. Information obtained – such as universities attended by highly successful employees – can help HR focus recruiting efforts accordingly. Additionally, Strategic Enterprise Management applications help a company translate corporate-level goals, such as profit and margin share targets, into operational decisions, such as production plans and workforce levels.
Another example of data mining, often called the market basket analysis, relates to its use in retail sales. If a clothing store records the purchases of customers, a data mining system could identify those customers who favor silk shirts over cotton ones. Although some explanations of relationships may be difficult, taking advantage of it is easier. The example deals with association rules within transaction-based data. Not all data are transaction based and logical, or inexact rules may also be present within a database.
Market basket analysis has also been used to identify the purchase patterns of the Alpha Consumer. Alpha Consumers are people that play a key role in connecting with the concept behind a product, then adopting that product, and finally validating it for the rest of society. Analyzing the data collected on this type of user has allowed companies to predict future buying trends and forecast supply demands.
Data mining is a highly effective tool in the catalog marketing industry. Catalogers have a rich database of history of their customer transactions for millions of customers dating back a number of years. Data mining tools can identify patterns among customers and help identify the most likely customers to respond to upcoming mailing campaigns.
Data mining for business applications is a component which needs to be integrated into a complex modeling and decision making process. Reactive business intelligence (RBI) advocates a "holistic" approach that integrates data mining, modeling, and interactive visualization into an end-to-end discovery and continuous innovation process powered by human and automated learning.
An example of data mining related to an integrated-circuit production line is described in the paper "Mining IC Test Data to Optimize VLSI Testing." In this paper, the application of data mining and decision analysis to the problem of die-level functional testing is described. Experiments mentioned demonstrate the ability to apply a system of mining historical die-test data to create a probabilistic model of patterns of die failure. These patterns are then utilized to decide, in real time, which die to test next and when to stop testing. This system has been shown, based on experiments with historical test data, to have the potential to improve profits on mature IC products.
Science and engineering
In the study of human genetics, sequence mining helps address the important goal of understanding the mapping relationship between the inter-individual variations in human DNA sequence and the variability in disease susceptibility. In simple terms, it aims to find out how the changes in an individual's DNA sequence affects the risks of developing common diseases such as cancer, which is of great importance to improving methods of diagnosing, preventing, and treating these diseases. The data mining method that is used to perform this task is known as multifactor dimensionality reduction.
In the area of electrical power engineering, data mining methods have been widely used for condition monitoring of high voltage electrical equipment. The purpose of condition monitoring is to obtain valuable information on, for example, the status of the insulation (or other important safety-related parameters). Data clustering techniques – such as the self-organizing map (SOM), have been applied to vibration monitoring and analysis of transformer on-load tap-changers (OLTCS). Using vibration monitoring, it can be observed that each tap change operation generates a signal that contains information about the condition of the tap changer contacts and the drive mechanisms. Obviously, different tap positions will generate different signals. However, there was considerable variability amongst normal condition signals for exactly the same tap position. SOM has been applied to detect abnormal conditions and to hypothesize about the nature of the abnormalities.
Data mining methods have also been applied to dissolved gas analysis (DGA) in power transformers. DGA, as a diagnostics for power transformers, has been available for many years. Methods such as SOM has been applied to analyze generated data and to determine trends which are not obvious to the standard DGA ratio methods (such as Duval Triangle).
Another example of data mining in science and engineering is found in educational research, where data mining has been used to study the factors leading students to choose to engage in behaviors which reduce their learning, and to understand factors influencing university student retention. A similar example of social application of data mining is its use in expertise finding systems, whereby descriptors of human expertise are extracted, normalized, and classified so as to facilitate the finding of experts, particularly in scientific and technical fields. In this way, data mining can facilitate institutional memory.
In adverse drug reaction surveillance, the Uppsala Monitoring Centre has, since 1998, used data mining methods to routinely screen for reporting patterns indicative of emerging drug safety issues in the WHO global database of 4.6 million suspected adverse drug reaction incidents. Recently, similar methodology has been developed to mine large collections of electronic health records for temporal patterns associating drug prescriptions to medical diagnoses.
Data mining of government records – particularly records of the justice system (i.e. courts, prisons) – enables the discovery of systemic human rights violations in connection to generation and publication of invalid or fraudulent legal records by various government agencies.
Medical data mining
In 2011, the case of Sorrell v. IMS Health, Inc., decided by the Supreme Court of the United States, ruled that Pharmacies may share information with outside companies. This practice was authorized under the 1st Amendment of the Constitution, protecting the "freedom of speech."
Spatial data mining
Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to visualization and data analysis. Particularly, most contemporary GIS have only very basic spatial analysis functionality. The immense explosion in geographically referenced data occasioned by developments in IT, digital mapping, remote sensing, and the global diffusion of GIS emphasizes the importance of developing data-driven inductive approaches to geographical analysis and modeling.
Data mining offers great potential benefits for GIS-based applied decision-making. Recently, the task of integrating these two technologies has become of critical importance, especially as various public and private sector organizations possessing huge databases with thematic and geographically referenced data begin to realize the huge potential of the information contained therein. Among those organizations are:
- offices requiring analysis or dissemination of geo-referenced statistical data
- public health services searching for explanations of disease clustering
- environmental agencies assessing the impact of changing land-use patterns on climate change
- geo-marketing companies doing customer segmentation based on spatial location.
Geospatial data repositories tend to be very large. Moreover, existing GIS datasets are often splintered into feature and attribute components that are conventionally archived in hybrid data management systems. Algorithmic requirements differ substantially for relational (attribute) data management and for topological (feature) data management. Related to this is the range and diversity of geographic data formats, which present unique challenges. The digital geographic data revolution is creating new types of data formats beyond the traditional "vector" and "raster" formats. Geographic data repositories increasingly include ill-structured data, such as imagery and geo-referenced multi-media.
There are several critical research challenges in geographic knowledge discovery and data mining. Miller and Han offer the following list of emerging research topics in the field:
- Developing and supporting geographic data warehouses (GDW's): Spatial properties are often reduced to simple aspatial attributes in mainstream data warehouses. Creating an integrated GDW requires solving issues of spatial and temporal data interoperability – including differences in semantics, referencing systems, geometry, accuracy, and position.
- Better spatio-temporal representations in geographic knowledge discovery: Current geographic knowledge discovery (GKD) methods generally use very simple representations of geographic objects and spatial relationships. Geographic data mining methods should recognize more complex geographic objects (i.e. lines and polygons) and relationships (i.e. non-Euclidean distances, direction, connectivity, and interaction through attributed geographic space such as terrain). Furthermore, the time dimension needs to be more fully integrated into these geographic representations and relationships.
- Geographic knowledge discovery using diverse data types: GKD methods should be developed that can handle diverse data types beyond the traditional raster and vector models, including imagery and geo-referenced multimedia, as well as dynamic data types (video streams, animation).
Sensor data mining
Wireless sensor networks can be used for facilitating the collection of data for spatial data mining for a variety of applications such as air pollution monitoring. A characteristic of such networks is that nearby sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires the techniques for in-network data aggregation and mining. By measuring the spatial correlation between data sampled by different sensors, a wide class of specialized algorithms can be developed to develop more efficient spatial data mining algorithms.
Visual data mining
In the process of turning from analogical into digital, large data sets have been generated, collected, and stored discovering statistical patterns, trends and information which is hidden in data, in order to build predictive patterns. Studies suggest visual data mining is faster and much more intuitive than is traditional data mining.
Music data mining
Data mining techniques, and in particular co-occurrence analysis, has been used to discover relevant similarities among music corpora (radio lists, CD databases) for the purpose of classifying music into genres in a more objective manner.
Data mining has been used to stop terrorist programs under the U.S. government, including the Total Information Awareness (TIA) program, Secure Flight (formerly known as Computer-Assisted Passenger Prescreening System (CAPPS II)), Analysis, Dissemination, Visualization, Insight, Semantic Enhancement (ADVISE), and the Multi-state Anti-Terrorism Information Exchange (MATRIX). These programs have been discontinued due to controversy over whether they violate the 4th Amendment to the United States Constitution, although many programs that were formed under them continue to be funded by different organizations or under different names.
In the context of combating terrorism, two particularly plausible methods of data mining are "pattern mining" and "subject-based data mining".
"Pattern mining" is a data mining method that involves finding existing patterns in data. In this context patterns often means association rules. The original motivation for searching association rules came from the desire to analyze supermarket transaction data, that is, to examine customer behavior in terms of the purchased products. For example, an association rule "beer ⇒ potato chips (80%)" states that four out of five customers that bought beer also bought potato chips.
In the context of pattern mining as a tool to identify terrorist activity, the National Research Council provides the following definition: "Pattern-based data mining looks for patterns (including anomalous data patterns) that might be associated with terrorist activity — these patterns might be regarded as small signals in a large ocean of noise." Pattern Mining includes new areas such a Music Information Retrieval (MIR) where patterns seen both in the temporal and non temporal domains are imported to classical knowledge discovery search methods.
Subject-based data mining
"Subject-based data mining" is a data mining method involving the search for associations between individuals in data. In the context of combating terrorism, the National Research Council provides the following definition: "Subject-based data mining uses an initiating individual or other datum that is considered, based on other information, to be of high interest, and the goal is to determine what other persons or financial transactions or movements, etc., are related to that initiating datum."
Knowledge discovery "On the Grid" generally refers to conducting knowledge discovery in an open environment using grid computing concepts, allowing users to integrate data from various online data sources, as well make use of remote resources, for executing their data mining tasks. The earliest example was the Discovery Net, developed at Imperial College London, which won the “Most Innovative Data-Intensive Application Award” at the ACM SC02 (Supercomputing 2002) conference and exhibition, based on a demonstration of a fully interactive distributed knowledge discovery application for a bioinformatics application. Other examples include work conducted by researchers at the University of Calabria, who developed a Knowledge Grid architecture for distributed knowledge discovery, based on grid computing.
Reliability / Validity
Data mining can be misused, and can also unintentionally produce results which appear significant but which do not actually predict future behavior and cannot be reproduced on a new sample of data. See Data snooping, Data dredging.
Privacy concerns and ethics
Some people believe that data mining itself is ethically neutral. It is important to note that the term "data mining" has no ethical implications, but is often associated with the mining of information in relation to peoples' behavior (ethical and otherwise). To be precise, data mining is a statistical method that is applied to a set of information (i.e. a data set). Associating these data sets with people is an extreme narrowing of the types of data that are available in today's technological society. Examples could range from a set of crash test data for passenger vehicles, to the performance of a group of stocks. These types of data sets make up a great proportion of the information available to be acted on by data mining methods, and rarely have ethical concerns associated with them. However, the ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics. In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns.
Data mining requires data preparation which can uncover information or patterns which may compromise confidentiality and privacy obligations. A common way for this to occur is through data aggregation. Data aggregation involves combining data together (possibly from various sources) in a way that facilitates analysis (but that also might make identification of private, individual-level data deducible or otherwise apparent). This is not data mining per se, but a result of the preparation of data before – and for the purposes of – the analysis. The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous.
It is recommended that an individual is made aware of the following before data are collected:
- the purpose of the data collection and any (known) data mining projects
- how the data will be used
- who will be able to mine the data and use the data and their derivatives
- the status of security surrounding access to the data
- how collected data can be updated.
In America, privacy concerns have been addressed to some extent by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act (HIPAA). The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses. According to an article in Biotech Business Week', "'[i]n practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena,' says the AAHC. More importantly, the rule's goal of protection through informed consent is undermined by the complexity of consent forms that are required of patients and participants, which approach a level of incomprehensibility to average individuals." This underscores the necessity for data anonymity in data aggregation and mining practices.
Data may also be modified so as to become anonymous, so that individuals may not readily be identified. However, even "de-identified"/"anonymized" data sets can potentially contain enough information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.
Free open-source data mining software and applications
- Carrot2: Text and search results clustering framework.
- Chemicalize.org: A chemical structure miner and web search engine.
- ELKI: A university research project with advanced cluster analysis and outlier detection methods written in the Java language.
- GATE: a natural language processing and language engineering tool.
- JHepWork: Java cross-platform data analysis framework developed at Argonne National Laboratory.
- KNIME: The Konstanz Information Miner, a user friendly and comprehensive data analytics framework.
- NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python language.
- Orange: A component-based data mining and machine learning software suite written in the Python language.
- R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU project.
- RapidMiner: An environment for machine learning and data mining experiments.
- UIMA: The UIMA (Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video – originally developed by IBM.
- Weka: A suite of machine learning software applications written in the Java programming language.
- ML-Flex: A software package that enables users to integrate with third-party machine-learning packages written in any programming language, execute classification analyses in parallel across multiple computing nodes, and produce HTML reports of classification results.
Commercial data-mining software and applications
- Angoss KnowledgeSTUDIO: data mining tool provided by Angoss.
- IBM SPSS Modeler: data mining software provided by IBM.
- KXEN Modeler: data mining tool provided by KXEN.
- Microsoft Analysis Services: data mining software provided by Microsoft.
- Salford Predictive Modeler: data mining software provided by Salford Systems.
- SAS Enterprise Miner: data mining software provided by the SAS Institute.
- STATISTICA Data Miner: data mining software provided by StatSoft.
- Oracle Data Mining: data mining software by Oracle.
- Clarabridge: enterprise class text analytics solution.
- LIONsolver: an integrated software application for data mining, business intelligence, and modeling that implements the Learning and Intelligent OptimizatioN (LION) approach.
Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, preferences and views of data miners. Some of these reports include:
- Forrester Research 2010 Predictive Analytics and Data Mining Solutions report
- Gartner 2008 "Magic Quadrant" report
- Haughton et al.'s 2003 Review of Data Mining Software Packages in The American Statistician
- Robert A. Nisbet's 2006 Three Part Series of articles "Data Mining Tools: Which One is Best For CRM?"
- 2011 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
- Application domains
- Application examples
- Related topics
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