A SEQUENCE-ELEMENT-BASED HIERARCHICAL CLUSTERING ALGORITHM FOR CATEGORICAL SEQUENCE DATA

Seung-Joon Oh () and Jae-Yearn Kim ()
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Seung-Joon Oh: Department of Industrial Engineering, Hanyang University, 17 Haengdang-Dong, Sungdong-Ku, Seoul, 133-791, South Korea
Jae-Yearn Kim: Department of Industrial Engineering, Hanyang University, 17 Haengdang-Dong, Sungdong-Ku, Seoul, 133-791, South Korea

International Journal of Information Technology & Decision Making (IJITDM), 2005, vol. 04, issue 01, 81-96

Abstract: Recently, there has been enormous growth in the amount of commercial and scientific data, such as protein sequences, retail transactions, and web-logs. Such datasets consist of sequence data that have an inherent sequential nature. However, few existing clustering algorithms consider sequentiality. In this paper, we study how to cluster these sequence datasets. We propose a new similarity measure to compute the similarity between two sequences. In the proposed measure, subsets of a sequence are considered, and the more identical subsets there are, the more similar the two sequences. In addition, we propose a hierarchical clustering algorithm and an efficient method for measuring similarity. Using a splice dataset and synthetic datasets, we show that the quality of clusters generated by our proposed approach is better than that of clusters produced by traditional clustering algorithms.

Keywords: Data mining; hierarchical clustering; sequences; similarity measure (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1142/S0219622005001398

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