Clustering categorical data by utilizing the correlated-force ensemble

Kun-Ta Chuang, Ming Syan Chen

研究成果: Paper同行評審

5 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁面269-278
頁數10
出版狀態Published - 2004 一月 1
事件Proceedings of the Fourth SIAM International Conference on Data Mining - Lake Buena Vista, FL, United States
持續時間: 2004 四月 222004 四月 24

Other

OtherProceedings of the Fourth SIAM International Conference on Data Mining
國家United States
城市Lake Buena Vista, FL
期間04-04-2204-04-24

All Science Journal Classification (ASJC) codes

  • Mathematics(all)

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