Multi-level organization and summarization of the discovered rules

Abstract

Many existing data mining techniques often produce a large number of rules, which make it very difficult for manual inspection of the rules to identify those interesting ones. This problem represents a major gap between the results of data mining and the understanding and use of the mining results. In this paper, we argue that the key problem is not with the large number of rules because if there are indeed many rules that exist in data, they should be discovered. The man problem is with our inability to organize, summarize and present the rules in such a way that they can be easily analyzed by the user. In this paper, we propose a technique to intuitively organize and summarize the discovered rules. With this organization, the discovered rules can be presented to the user in the way as we think and talk about knowledge in our dally lives. This organization also allows the user to view the discovered rules at different levels of details, and to focus his/her attention on those interesting aspects. This paper presents this technique and uses it to organize, summarize and present the knowledge embedded in a decision tree, and a set of association rules. Experiment results and practical applications show that the technique is both intuitive and effective.