Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6939449 | Pattern Recognition | 2018 | 40 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Xiaojun Chen, Min Yang, Joshua Zhexue Huang, Zhong Ming,