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Proceedings Paper

Distance-based clustering algorithm of association rules on various types of attributes
Author(s): Ying Hu; Jie Yang; ZhiQiang Yu
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Paper Abstract

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 numerilzed attributes being computed straightly, we propose an approach to normalized 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. Tow simple examples are also provided to demonstrate the better result of the clustering algorithm in the end of the paper.

Paper Details

Date Published: 24 September 2001
PDF: 5 pages
Proc. SPIE 4554, Object Detection, Classification, and Tracking Technologies, (24 September 2001); doi: 10.1117/12.441642
Show Author Affiliations
Ying Hu, Shanghai Jiaotong Univ. (China)
Jie Yang, Shanghai Jiaotong Univ. (China)
ZhiQiang Yu, Shanghai Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 4554:
Object Detection, Classification, and Tracking Technologies
Jun Shen; Sharatchandra Pankanti; Runsheng Wang, Editor(s)

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