Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Mixed Co-clustering Algorithm Based on Information Bottleneck

  • Liu, Yongli (School of Computer Science and Technology, Henan Polytechnic University) ;
  • Duan, Tianyi (School of Computer Science and Technology, Henan Polytechnic University) ;
  • Wan, Xing (School of Computer Science and Technology, Henan Polytechnic University) ;
  • Chao, Hao (School of Computer Science and Technology, Henan Polytechnic University)
  • Received : 2017.01.06
  • Accepted : 2017.03.04
  • Published : 2017.12.31
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Abstract

Fuzzy co-clustering is sensitive to noise data. To overcome this noise sensitivity defect, possibilistic clustering relaxes the constraints in FCM-type fuzzy (co-)clustering. In this paper, we introduce a new possibilistic fuzzy co-clustering algorithm based on information bottleneck (ibPFCC). This algorithm combines fuzzy co-clustering and possibilistic clustering, and formulates an objective function which includes a distance function that employs information bottleneck theory to measure the distance between feature data point and feature cluster centroid. Many experiments were conducted on three datasets and one artificial dataset. Experimental results show that ibPFCC is better than such prominent fuzzy (co-)clustering algorithms as FCM, FCCM, RFCC and FCCI, in terms of accuracy and robustness.

Keywords

Acknowledgement

Supported by : Natural Science Foundation of China

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