Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Convergence of a block coordinate descent method for nondifferentiable minimization
Journal of Optimization Theory and Applications
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Computers and Operations Research
Co-Clustering Tags and Social Data Sources
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Analyzing communities and their evolutions in dynamic social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
On evolutionary spectral clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Information Processing and Management: an International Journal
An efficient algorithm for a class of fused lasso problems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
Mining order-preserving submatrices from probabilistic matrices
ACM Transactions on Database Systems (TODS)
A Probabilistic Latent Semantic Analysis Model for Coclustering the Mouse Brain Atlas
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hi-index | 0.00 |
Traditional co-clustering methods identify block structures from static data matrices. However, the data matrices in many applications are dynamic; that is, they evolve smoothly over time. Consequently, the hidden block structures embedded into the matrices are also expected to vary smoothly along the temporal dimension. It is therefore desirable to encourage smoothness between the block structures identified from temporally adjacent data matrices. In this paper, we propose an evolutionary co-clustering formulation for identifying co-cluster structures from time-varying data. The proposed formulation encourages smoothness between temporally adjacent blocks by employing the fused Lasso type of regularization. Our formulation is very flexible and allows for imposing smoothness constraints over only one dimension of the data matrices, thereby enabling its applicability to a large variety of settings. The optimization problem for the proposed formulation is non-convex, non-smooth, and non-separable. We develop an iterative procedure to compute the solution. Each step of the iterative procedure involves a convex, but non-smooth and non-separable problem. We propose to solve this problem in its dual form, which is convex and smooth. This leads to a simple gradient descent algorithm for computing the dual optimal solution. We evaluate the proposed formulation using the Allen Developing Mouse Brain Atlas data. Results show that our formulation consistently outperforms methods without the temporal smoothness constraints.