A Mixed Co-clustering Algorithm Based on Information Bottleneck

Yongli Liu; Tianyi Duan; Xing Wan; Hao Chao

A Mixed Co-clustering Algorithm Based on Information Bottleneck

Yongli Liu, Tianyi Duan, Xing Wan, Hao Chao, Journal of Information Processing Systems Vol. 13, No. 6, pp. 1467-1486, Dec. 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

[APA Style]

Liu, Y., Duan, T., Wan, X., & Chao, H. (2017). A Mixed Co-clustering Algorithm Based on Information Bottleneck. Journal of Information Processing Systems, 13(6), 1467-1486. DOI: 10.3745/JIPS.01.0019.

[IEEE Style]

Y. Liu, T. Duan, X. Wan, H. 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. 2017. 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.