English, Article edition: A SEQUENCE-ELEMENT-BASED HIERARCHICAL CLUSTERING ALGORITHM FOR CATEGORICAL SEQUENCE DATA SEUNG-JOON OH; JAE-YEARN KIM

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Bookmark: http://trove.nla.gov.au/version/36274
Physical Description
  • article
Language
  • English

Edition details

Title
  • A SEQUENCE-ELEMENT-BASED HIERARCHICAL CLUSTERING ALGORITHM FOR CATEGORICAL SEQUENCE DATA
Author
  • SEUNG-JOON OH
  • JAE-YEARN KIM
Physical Description
  • article
Notes
  • 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.
  • Data mining, hierarchical clustering, sequences, similarity measure
  • RePEc:wsi:ijitdm:v:04:y:2005:i:01:p:81-96
Language
  • English
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