Post-Analysis of Learned Rules

Bing Liu, Wynne Hsu

Rule induction research implicitly assumes that after producing the rules from a dataset, these rules will be used directly by an expert system or a human user. In real-life applications, the situation may not be as simple as that, particularly, when the user of the rules is a human being. The human user almost always has some previous concepts or knowledge about the domain represented by the dataset. Naturally, he/she wishes to know how the new rules compare with his/her existing knowledge. In dynamic domains where the rules may change over time, it is important to know what the changes are. These aspects of research have largely been ignored in the past. With the increasing use of machine learning techniques in practica1 applications such as data mining, this issue of post analysis of rules warrants greater emphasis and attention. In this paper, we propose a technique to deal with this problem. A system has been implemented to perform the post analysis of classification rules generated by systems such as C4.5. The proposed technique is general and highly interactive. It will be particularly useful in data mining and data analysis.

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