Clustering categorical data by utilizing the correlated-force ensemble

Kun-Ta Chuang, Ming Syan Chen

Research output: Contribution to conferencePaperpeer-review

6 Citations (Scopus)


We explore in this paper a novel clustering algorithm, named CORE (standing for CORrelated-Force Ensemble), for categorical data. In general, it is more difficult to perform clustering on categorical data than on numerical data due to the absence of the ordered property in the former. Though several clustering algorithms which concentrate on categorical date were proposed, acquiring the desirable quality remains a challenging issue. Note that there is significance hidden in the correlation between attribute values that can be explored to aid clustering, especially extracting clusters in the high dimensional data. Therefore by employing the concept of correlated-force ensemble, clusters which consist of the highly correlated set of nominal attribute values, can be acquired by the proposed algorithm, CORE. As validated by variant real datasets, it is shown in our experimental results that algorithm CORE significantly outperforms the prior works.

Original languageEnglish
Number of pages10
Publication statusPublished - 2004 Jan 1
EventProceedings of the Fourth SIAM International Conference on Data Mining - Lake Buena Vista, FL, United States
Duration: 2004 Apr 222004 Apr 24


OtherProceedings of the Fourth SIAM International Conference on Data Mining
Country/TerritoryUnited States
CityLake Buena Vista, FL

All Science Journal Classification (ASJC) codes

  • Mathematics(all)


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