TWCC: Automated Two-way Subspace Weighting Partitional Co-Clustering. (April 2018)

• Record Type: Journal Article
• Authors: Chen, Xiaojun; Yang, Min; Zhexue Huang, Joshua; Ming, Zhong
• Is Part Of: Pattern recognition. Volume 76(2018:Apr.)
• Page Start: 404
• Page End: 415
• Publication Date: 2018-04
• Subjects: Data mining -- Co-clustering -- Subspace clustering -- Variable weighting; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.10.026
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A co-clustering method TWCC was proposed, in which two types of weights are automatically computed. It's the first two-way subspace weighting partitional co-clustering method. It can simultaneously weight data from two ways for co-clustering. Experimental results on both synthetic and real-life data sets were presented to verify TWCC. Abstract: A two-way subspace weighting partitional co-clustering method TWCC is proposed. In this method, two types of subspace weights are introduced to simultaneously weight the data in two ways, i.e., columns on row clusters and rows on column clusters. An objective function that uses the two types of weights in the distance function to determine the co-clusters of data is defined, and an iterative TWCC co-clustering algorithm to optimize the objective function is proposed, in which the two types of subspace weights are automatically computed. A series of experiments on both synthetic and real-life data were conducted to investigate the properties of TWCC, compare the two-way clustering results of TWCC with those of eight co-clustering algorithms, and compare one-way clustering results of TWCC with those of six clustering algorithms. The results have shown that TWCC is robust and effective for large high-dimensional data.