Comments/Suggestions

• WEKA
  WEKA is an open-source data mining package containing a full collection of machine learning algorithms for solving various data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code. It includes several implemented schemes for classification, association rule discovery, clustering, prediction, etc. The full distribution of WEKA as well as additional information and supporting material can be found at the official WEKA Web site. The site also includes additional data (from the UCI data repository) already converted into the ARFF format which is used by WEKA.

• Clustering and Profile Generation Tools
  This is set of programs developed here for clustering and generation of profiles based on the results of clustering. The set also includes some programs to assist in characterizing the generated clusters. The documentation for each program and some example data sets are included in the distribution. All of these programs and the documentation are included in a single Zip Archive.

• Magnum Opus
  Magnum Opus is a tool for finding association rules from data. It uses a highly efficient search algorithm for fast association rule discovery and does not rely on sparse data for efficient processing. More information on Magnum Opus as well as an evaluation download version can be found from the G.I. Webb & Associates.

• See5/C5.0
  See5 is the commercial version of the C4.5 decision tree algorithm developed by Ross Quinlan. See5/C5.0 classifiers are expressed as decision trees or sets of if-then rules. RuleQuest provides C source code so that classifiers constructed by See5/C5.0 can be embedded in your own systems. More information on See5/C5.0 as well as an evaluation download version can be found from the RuleQuest Site.

• Cubist
  Yet another program from RuleQuest. Cubist builds rule-based predictive models that output values, complementing See5/C5.0 that predicts categories. For instance, See5/C5.0 might classify the yield from some process as "high", "medium", or "low", whereas Cubist would output a number such as 73%. Information on Cubist and the evaluation download version can be found from the RuleQuest Site

•  

• CBA
  CBA is a data mining tool for the discovery of association rules and for classification. The classification technique used in CBA is based on using a subset of associations discovered. CBA implements two versions of Apriori algorithm (one using a single minimum support parameter, and another using multiple minimum support at different levels). It also includes features for visualizing association rules using a tree structure.

  • More Information on CBA can be found from the DMII Site.

  • Local downloaded for the full educational version of CBA can be found here (zip archive).