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DM Software

There is a plethora of software produced by research individuals and groups, but also by commercial companies. These span from individual techniques (such as C5.0 program from Quinlan) to data mining suites like WEKA, Kepler, Clementine, which include versions of different techniques capable of solving very different tasks. We present here only free data mining suites. A comprehensive overview of data mining and knowledge discovery software (both commercial and free) can be found on KDDnuggets site.

Free Data Mining suites

WEKA - a collection of machine learning algorithms for solving data mining problems written in Java. It runs on almost any platform. The algorithms can either be applied directly to a dataset or can be called from one's own Java code. Weka is also well-suited for developing new machine learning schemes. It is an open source software issued under the GNU General Public License. Techniques available include:

MLC++ library
MLC++ is a library of C++ classes for supervised machine learning. The MLC++ utilities were created using the library. MLC++ provides general machine learning algorithms that can be used by end users, analysts, professionals, and researchers. The main objective is to provide users with a wide variety of tools that can help mine data, accelerate development of new mining algorithms, increase software reliability, provide comparison tools, and display information visually. MLC++ can be installed on different platforms: SUN Solaris, SGI Irix, Windows NT, possibly also under Linux.


TMiner
TMiner Personal Edition is a collection of algorithms for mining data from relational databases. It can be used for mining association rules, building classifiers and clusterings using JDBC (the standard Java call-level interface). It has been tested on different DBMSs such as Oracle 8i, IBM DB2 UDB and InterBase. TMiner is developed by IdBIS group (Fernando Berzal Galiano and Juan Carlos Cubero Talavera).


© 2001 LIS - Rudjer Boskovic Institute
Last modified: February 01 2002 13:31:56.