Clustering item data sets with association-taxonomy similarity

Ching Huang Yun, Kun Ta Chuang, Ming Syan Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


We explore in this paper the efficient clustering of item data. Different from those of the traditional data, the features of item data are known to be of high dimensionality and sparsity. In view of the features of item data, we devise in this paper a novel measurement, called the association-taxonomy similarity, and utilize this measurement to perform the clustering. With this association-taxonomy similarity measurement, we develop an efficient clustering algorithm, called algorithm AT (standing for Association-Taxonomy), for item data. Two validation indexes based on association and taxonomy properties are also devised to assess the quality of clustering for item data. As validated by the real dataset, it is shown by our experimental results that algorithm AT devised in this paper significantly out-performs the prior works in the clustering quality as measured by the validation indexes, indicating the usefulness of association-taxonomy similarity in item data clustering.

Original languageEnglish
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Number of pages4
Publication statusPublished - 2003 Dec 1
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: 2003 Nov 192003 Nov 22

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other3rd IEEE International Conference on Data Mining, ICDM '03
CountryUnited States
CityMelbourne, FL

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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