Association Classification Based on Compactness of Rules

Authors:
Qiang Niu;Shi-Xiong Xia;Lei Zhang
Affiliations:
-;-;-
Venue:
WKDD '09 Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining
Year:
2009

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Abstract

Associative classification has high classification accuracy and strong flexibility. However, it still suffers from overfitting since the classification rules satisfied both minimum support and minimum confidence are returned as strong association rules back to the classifier. In this paper, we propose a new association classification method based on compactness of rules, it extends Apriori Algorithm，which considers the interestingness, importance, overlapping relationships among rules. At last, experimental results shows that the algorithm has better classification accuracy in comparison with CBA and CMAR are highly comprehensible and scalable.