CBA (v2.0) -- Classification Based on Associations CBA mines association rules and builds accurate classifiers using a subset of association rules. See the paper in KDD-98, which can also be downloaded from http://www.comp.nus.edu.sg/~liub. CBA v2.0 is the second version of CBA. This version has the following new features: 1) Mining association rules with multiple minimum supports (SIGKDD'99) 2) Better classification accuracy -- After testing against the 26 datasets used in our KDD-98 paper (the datasets are all from UCI repository for Machine Learning), CBA v2.0 achieves the average error rate of 15.2% over these datasets. For these data sets, C4.5 (release 8) achieves the error rate of 16.7% (c4.5rules) and 17.3% (c4.5tree), while for CBA v1.0, it is 15.8%. See the detailed comparision in: http://www.comp.nus.edu.sg/~dm2/result.html -- The better result is obtained due to the use of multiple minimum class supports (rules with different classes have different automatically assigned minimum supports). The multiple minimum class supports scheme is a special case of multiple minimum supports above. 3) Faster rule mining 4) A HTML viewer to help user analyze the discovered rules CBA v2.0 can be downloaded from: http://www.comp.nus.edu.sg/~dm2 Furthermore, we have also made a post-analysis system downloadable. The system is called Interestingness Analysis System (IAS). IAS helps the user find interesting (conforming and unexpected) rules based on his/her existing domain knowledge. Regards Bing Liu Email: liub@comp.nus.edu.sg Web: http://www.comp.nus.edu.sg/~liub
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