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KDD
2005
ACM

Co-clustering by block value decomposition

14 years 10 months ago
Co-clustering by block value decomposition
Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data analysis is to find the hidden block structure of the data matrix. In this paper, we present a new coclustering framework, block value decomposition(BVD), for dyadic data, which factorizes the dyadic data matrix into three components, the row-coefficient matrix R, the block value matrix B, and the column-coefficient matrix C. Under this framework, we focus on a special yet very popular case ? non-negative dyadic data, and propose a specific novel co-clustering algorithm that iteratively computes the three decomposition matrices based on the multiplicative updating rules. Extensive experimental evaluations also demonstrate the effectiveness and potential of this framework as well as the specific algorithms for co-clustering, and in particular, for discovering the hidden block structure in the dyadic data. Categ...
Bo Long, Zhongfei (Mark) Zhang, Philip S. Yu
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2005
Where KDD
Authors Bo Long, Zhongfei (Mark) Zhang, Philip S. Yu
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