Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

Tomoki Tokuda, Junichiro Yoshimoto, Yu Shimizu +8

We propose a novel method for multiple clustering that assumes a co-clustering structure (partitions in both rows and columns of the data matrix) in each view. The new method is ap…