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http://dx.doi.org/10.3745/JIPS.01.0019

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)
Publication Information
Journal of Information Processing Systems / v.13, no.6, 2017 , pp. 1467-1486 More about this Journal
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
Co-clustering; F-Measure; Fuzzy Clustering; Information Bottleneck; Objective Function;
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