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PHC: A fast partition and hierarchy-based clustering algorithm

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Abstract

Cluster analysis is a process to classify data in a specified data set. In this field, much attention is paid to high-efficiency clustering algorithms. In this paper, the features in the current partition-based and hierarchy-based algorithms are reviewed, and a new hierarchy-based algorithm PHC is proposed by combining advantages of both algorithms, which uses the cohesion and the closeness to amalgamate the clusters. Compared with similar algorithms, the performance of PHC is improved, and the quality of clustering is guaranteed. And both the features were proved by the theoretic and experimental analyses in the paper.

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Authors and Affiliations

  1. Department of Computing and Information Technology, Fudan University, 200433, Shanghai, P.R. China

    HaoFeng Zhou, QingQing Yuan, ZunPing Cheng & BaiLe Shi

Authors
  1. HaoFeng Zhou
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  2. QingQing Yuan
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  3. ZunPing Cheng
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  4. BaiLe Shi
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Corresponding author

Correspondence to HaoFeng Zhou.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant No.69933010).

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Zhou, H., Yuan, Q., Cheng, Z. et al. PHC: A fast partition and hierarchy-based clustering algorithm. J. Comput. Sci. & Technol. 18, 407–411 (2003). https://doi.org/10.1007/BF02948913

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  • DOI: https://doi.org/10.1007/BF02948913

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