A Mixed Co-clustering Algorithm Based on Information Bottleneck

Yongli Liu, Tianyi Duan, Xing Wan and Hao Chao
Volume: 13, No: 6, Page: 1467 ~ 1486, Year: 2017
10.3745/JIPS.01.0019
Keywords: Co-clustering, F-Measure, Fuzzy Clustering, Information Bottleneck, Objective Function
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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.

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Cite this article
IEEE Style
Yongli Liu, Tianyi Duan, Xing Wan, and Hao Chao, "A Mixed Co-clustering Algorithm Based on Information Bottleneck," Journal of Information Processing Systems, vol. 13, no. 6, pp. 1467~1486, 2017. DOI: 10.3745/JIPS.01.0019.

ACM Style
Yongli Liu, Tianyi Duan, Xing Wan, and Hao Chao, "A Mixed Co-clustering Algorithm Based on Information Bottleneck," Journal of Information Processing Systems, 13, 6, (2017), 1467~1486. DOI: 10.3745/JIPS.01.0019.