An association analysis approach to biclustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent Dirichlet Bayesian Co-Clustering
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
PAC-Bayesian Analysis of Co-clustering and Beyond
The Journal of Machine Learning Research
Mixed-membership naive Bayes models
Data Mining and Knowledge Discovery
Co-clustering with augmented data matrix
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Learning multiple models for exploiting predictive heterogeneity in recommender systems
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
On context-aware co-clustering with metadata support
Journal of Intelligent Information Systems
A levelwise spectral co-clustering algorithm for collaborative filtering
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Summarization-based mining bipartite graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Feature enriched nonparametric bayesian co-clustering
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
SPCF: a stepwise partitioning for collaborative filtering to alleviate sparsity problems
Journal of Information Science
Variational Bayes co-clustering with auxiliary information
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
Co-clustering with augmented matrix
Applied Intelligence
Mixtures of biased sentiment analysers
Advances in Data Analysis and Classification
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In recent years, co-clustering has emerged as a powerful data mining tool that can analyze dyadic data connecting two entities. However, almost all existing co-clustering techniques are partitional, and allow individual rows and columns of a data matrix to belong to only one cluster. Several current applications, such as recommendation systems and market basket analysis, can substantially benefit from a mixed membership of rows and columns. In this paper, we present Bayesian co-clustering (BCC) models, that allow a mixed membership in row and column clusters. BCC maintains separate Dirichlet priors for rows and columns over the mixed membership and assumes each observation to be generated by an exponential family distribution corresponding to its row and column clusters. We propose a fast variational algorithm for inference and parameter estimation. The model is designed to naturally handle sparse matrices as the inference is done only based on the non-missing entries. In addition to finding a co-cluster structure in observations, the model outputs a low dimensional co-embedding, and accurately predicts missing values in the original matrix. We demonstrate the efficacy of the model through experiments on both simulated and real data.