Our goal is to find salient bi-clusters from a given relational data matrix automatically. Salient bi-clusters are sub-matrices that have distinct values from other entries of the data matrix. Such bi-clusters often corresponds to informative subsets of the data; e.g. "good customer groups with best-selling items for them", and "specific gene clusters that are reactive for a specific treatment/chemicals".
Conventionally, bi-clustering requires us to specify the number of bi-clusters to be extracted before the analysis. Howeverit is generally difficult to know the number of bi-clusters before conducting an actual analysis.
Our proposed model enables us to forget about this specification of the number of bi-clusters. The model automatically infer an appropriate number of bi-clusters (up to infinite!) for the given data matrix, and performs effective bi-clustering. This model will help users to conduct "easy-to-go" bi-clustering for several situations.