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CBA : Classification Based on Association Ver 1.0 (Demo)


CBA is a data mining tool developed at School of Computing, National University of Singapore. Its main algorithm was presented as a plenary paper "Integrating Classification and Association Rule Mining" in the 4th International Conference on Knowledge Discovery and Data Mining (KDD-98 ), August 23-27, 1998, New York City, USA. CBA originally stands for Classification Based on Associations. However, it turns out that it is more powerful than simply producing an accurate classifier for prediction. It can also be used for mining various forms of association rules, and for text categorization or classification.

In summary, CBA has the following unique features:

1. Classification and prediction
  • Build accurate classifiers from relational data, where each record is described with a fixed number of attributes. This type of data is what traditional classification techniques use, e.g., decision tree, neural networks, and many others.

2. Mining association rules from relational data or transactional data

3. Text categorization and classification (single class, at this moment)

  • Build accurate classifiers from transactional data, where each data record has a variable number of items, e.g., items bought in a supermarket by a customer, or the keywords in a text document.

CBA also has many other features, e.g., cross-validation for evaluating classifiers, and rule and tree viewers (to view and to query the discovered rules).

CBA is the central component of our DM-II suite. The full version is available based on the follows:

Academic Version (Non-Commercial use): FREE
For commercial use: US$2000.00

If you are interested in incorporating CBA into your own product, please contact us.

Click HERE to proceed to the Download Page.

For more details on purchasing the CBA software, please contact us at DM-II.

IAS: Interestingness Analysis System Ver 1.0 (Beta Testing phase)

Description :

The rules generated by CBA engine could be further processed by this system. The system helps the user to find interesting/useful rules based on his/her existing knowledge about the domain. The interesting rules include conforming rules, and various types of unexpected rules.



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