Pentaho Data Mining (Weka)

Welcome to the community home for Pentaho Data Mining Community Edition (CE) also known as Weka. Pentaho Data Mining is a comprehensive set of tools for machine learning and data mining. Its broad suite of classification, regression, association rules and clustering algorithms can be used to help you understand the business better and also be exploited to improve future performance through predictive analytics.

Community Edition is self supported open source software. An Enterprise Edition (EE) of Pentaho Data Mining including technical support and managed upgrades is also available. For more information about EE or for screen shots and datasheets, visit Pentaho Data Mining EE on Pentaho's corporate site.

Recent News and Releases

- 05/11/12 New Weka 3.6.7 and 3.7.6 releases available, more info.
- 10/31/11 New Weka 3.6.6 and 3.7.5 releases available, more info.
- 06/30/11 New Weka 3.4.19, 3.6.5 and 3.7.4 releases available, more info.
- 06/30/11 Weka 3.6.5 - stable book 3rd ed. version now available.
- 06/30/11 Weka 3.4.19 - stable book 2nd ed. version is now available.
- 06/30/11 Weka 3.7.4 - development version now available.
 

Stable
Weka 3.6.7 GA (Book 3rd. ed. version) (Release Notes)
This is a stable version, created from the head of the 3.5 development code line, and corresponds to what is described in the Witten, Frank and Hall data mining book. The 3.6 code line will receive bug-fixes only (development of new features continues in 3.7). For a detailed list of improvements, please refer to the release notes.
- Download(s) - Source - Read me
- Documentation - Forum  

New Features since 3.4
- 35 new learning schemes
- 17 new filters
- Grouping of steps (MetaBean) in Knowledge Flow
- New SQL viewer and visualization plugin support in Explorer
- Area under ROC (AUC) evaluation type
- Relation-valued attributes (supports multi-instance learning)
- Support for incremental clusterers
- XML format for instances
- Text directory to ARFF tool
- Several new data generators

Weka 3.4.19 GA (Book 2nd ed. version) (Release Notes)
This is a patch release to Weka 3.4 containing a number of bug fixes. For a detailed list of improvements, please refer to the release notes. Because the 3rd ed. of the data mining book was published in January 2011, this is the last GA release from the 3.4 code line.
- Download(s) - Source - Read me
- Documentation - Forum  


 

In Development
Weka 3.7.x Development
This is the new development branch of Weka, continuing from 3.5.8 and will include new features as well as bug fixes. Weka 3.7.2 moved a lot of algorithms/tools out of the main Weka distribution and into "packages", managed by a new package management system. Information on the package management system can be found in the WekaManual.pdf included in the >=3.7.2 distribution and on the Weka Wiki:

- How to use the package manager wiki article.
- Package management system package structure wiki article.
- Packages can be browsed online here.
- Download(s) - Source - Read me
- Documentation - Forum  

New Features in 3.7.6

In core weka:
 
* Weka 3.7 is now GPL 3.0.
* Weka releases now available on Maven central
* Logistic now has an option to use conjugate gradient descent rather than quasi-Newton with BFGS updates.
* weka.classifiers.bayes.NaiveBayesMultinomialText - naive Bayes multinomial classifier that operates directly on string attributes.
* Appender component for the Knowledge Flow that can append sets of instances together.
* SubstringLabeler component for the Knowledge Flow that can use substring or regex matching on string attribute values to assign various user defined nominal values to a new "label" attribute.
* SubstringReplacer component for the Knowledge Flow that can replace substrings or regex matches with user supplied strings in string attribute values.
* Sorter component for the Knowledge Flow that implements a streaming merge sort that writes a sorted in-memory buffer to a file when full. Can sort descending or ascending on multiple attributes.
* DatabaseSaver can now truncate the target table if desired.
* Area under the precision-recall curve evaluation metric.
* Package manager's cache refresh mechanism is now much faster.
* Package manager now checks for new versions of existing packages on the server as well as entirely new packages.
* Random forest now has an option to print all the ensemble trees as part of its output.
 
 
In packages:
* cascadeKMeans package - weka.clusterers.CascadeSimpleKMeans, contributed by Martin Guetlein.
* weka.classifiers.functions.RBFRegressor added to the RBFNetwork package
* jsonFieldExtractor package - Knowledge Flow step to extract one or more fields from repeating blocks of JSON text into new attributes.
Upcoming Training
Quick Links

Helpful Links
- Frequently Asked Questions
- Mailing List
- Data Sets
- Download latest snapshot
- PDM community documentation
- Screenshots
- Partners
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Contribute to the Project

You can participate by contributing new code, reporting bugs, testing new releases, answering questions and more; Email us the proposed contribution and any other relevant details. Welcome to the team.
- Write a tech tip
- Report a bug in JIRA
- Answer posts on the forums
- Write some code