A Mixed Co-clustering Algorithm Based on Information Bottleneck
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초록
Fuzzy co-clustering is sensitive to noise data. To overcome this noise sensitivity defect, possibilistic clusteringrelaxes the constraints in FCM-type fuzzy (co-)clustering. In this paper, we introduce a new possibilistic fuzzyco-clustering algorithm based on information bottleneck (ibPFCC). This algorithm combines fuzzy coclusteringand possibilistic clustering, and formulates an objective function which includes a distance functionthat employs information bottleneck theory to measure the distance between feature data point and featurecluster centroid. Many experiments were conducted on three datasets and one artificial dataset. Experimentalresults show that ibPFCC is better than such prominent fuzzy (co-)clustering algorithms as FCM, FCCM,RFCC and FCCI, in terms of accuracy and robustness.
Abstract
Fuzzy co-clustering is sensitive to noise data. To overcome this noise sensitivity defect, possibilistic clusteringrelaxes the constraints in FCM-type fuzzy (co-)clustering. In this paper, we introduce a new possibilistic fuzzyco-clustering algorithm based on information bottleneck (ibPFCC). This algorithm combines fuzzy coclusteringand possibilistic clustering, and formulates an objective function which includes a distance functionthat employs information bottleneck theory to measure the distance between feature data point and featurecluster centroid. Many experiments were conducted on three datasets and one artificial dataset. Experimentalresults show that ibPFCC is better than such prominent fuzzy (co-)clustering algorithms as FCM, FCCM,RFCC and FCCI, in terms of accuracy and robustness.
참고문헌 (23)
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