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A fuzzy co-clustering algorithm for biomedical data

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  • Yongli Liu
  • Shuai Wu
  • Zhizhong Liu
  • Hao Chao

Abstract

Fuzzy co-clustering extends co-clustering by assigning membership functions to both the objects and the features, and is helpful to improve clustering accurarcy of biomedical data. In this paper, we introduce a new fuzzy co-clustering algorithm based on information bottleneck named ibFCC. The ibFCC formulates an objective function which includes a distance function that employs information bottleneck theory to measure the distance between feature data point and the feature cluster centroid. Many experiments were conducted on five biomedical datasets, and the ibFCC was compared with such prominent fuzzy (co-)clustering algorithms as FCM, FCCM, RFCC and FCCI. Experimental results showed that ibFCC could yield high quality clusters and was better than all these methods in terms of accuracy.

Suggested Citation

  • Yongli Liu & Shuai Wu & Zhizhong Liu & Hao Chao, 2017. "A fuzzy co-clustering algorithm for biomedical data," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-18, April.
    • Handle: RePEc:plo:pone00:0176536
    DOI: 10.1371/journal.pone.0176536
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    Cited by:

    1. Yongli Liu & Jingli Chen & Shuai Wu & Zhizhong Liu & Hao Chao, 2018. "Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-25, May.

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