Study on Distance-Based Clustering Algorithm of Association Rules on Various types of Attributes

摘要

Association rule clustering is one of the most important topics in data mining. This paper proposes a generalization of distance-based clustering algorithm of association rules on various types of attributes. Firstly, considering complex database with various data, we present numeralized processing to deal with rules on many kinds of attributes. Secondly, instead of these values of numeralized attributes being computed straightly, we propose an approach to normalize these attributes of association rules. Finally, with applying the numeralized as well as normalization methods, we present the generalization of clustering algorithm based on the different definitions of distances and diameters of rules. This algorithm can be used to handle the rules with attributes of different types and different scales, which extend the method of clustering in Ref.l. Two simple examples are also provided to demonstrate the better results of the clustering algorithm in the end of the paper.