Mining range associations for classification and characterization

摘要

In this paper, we propose a method that is able to derive rules involving range associations from numerical attributes, and to use such rules to build comprehensible classification and characterization (data summary) models. Our approach follows the classification association rule mining paradigm, where rules are generated in a way similar to association rule mining, but search is guided by rule consequents. This allows many credible rules, not just some dominant rules, to be mined from the data to build models. In so doing, we propose several sub-range analysis and rule formation heuristics to deal with numerical attributes. Our experiments show that our method is able to derive range-based rules that offer both accurate classification and comprehensible characterization for numerical data.