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Public_Domain_Software resources

Inducing Functional Dependencies from Relations

Includes source code, related research papers and associated work.

Association Rule Miner

Client-server Java based data mining software for mining association rules. Developed at University of Massachusetts.

Machine Learning Library in C++

MLC++ is a standard C++ library for supervised machine learning, with back-end and front-end tools for data mining tasks like Decision Trees, and Clustering. Information on legal issues, mailing lists, history, standards, platform support, and download instructions.

QuickMiner

Open Source creation of a data mining C++ procedure library. Initially focused on mining generalised association rules and generalised sequential patterns

WinMine Toolkit Home Page

By David Chickering at Microsoft Research. The WinMine Toolkit is a set of tools for Windows 2000/NT/XP that allow you to build statistical models from data. The majority of the tools are command-line executables that can be run in scripts.

Discovery of Multivalued Dependencies from Relations

Includes source code, related papers and associated projects.

DeFindIt Analysis and Reporting

Open source software for extraction and reporting using a powerful template tool. Deft combines declarative concepts of SQL with all of Perl's features. Requires Linux and Perl

FOIL and C4.5

Source code for decision tree algorithms from Ross Quinlan's homepage, available free for download.

ECOBWEB - Concept Formation Program

Source code for program for creation of hierarchical classification trees. Information about implementations, documentation, and related research papers.

CLUTO - Clustering Toolkit

A freely available software toolkit for clustering low- and high-dimensional data sets. It is well-suited for clustering data sets arising in many areas including information retrieval, customer purchasing transactions, science, and biology.

PAFI - Pattern Finding Toolkit

A freely available software toolkit for finding frequent patterns in diverse datasets. It contains highly efficient algorithms for finding patterns in transactional, sequential, and graph datasets.

Snob

Uses the Minimum Message Length (MML) principle to do mixture modeling. Mixture modeling concerns modeling a statistical distribution by a mixture of other distributions, and is also known as unsupervised concept learning in Artificial Intelligence. Links to related research papers and software.