MCNC: multi-channel nonparametric clustering from heterogeneous data

Bayesian nonparametric (BNP) models have recently become popular due to their flexibility in identifying the unknown number of clusters. However, they have difficulties handling heterogeneous data from multiple sources. Existing BNP methods either treat each of these sources independently - hence do not get benefits from the correlating information between them, or require to explicitly specify data sources as primary and context channels. In this paper, we present a BNP framework, termed MCNC, which has the ability to (1) discover co-patterns from multiple sources; (2) explore multi-channel data simultaneously and treat them equally; (3) automatically identify a suitable number of patterns from data; and (4) handle missing data. The key idea is to utilize a richer base measure of a BNP model being a product-space. We demonstrate our framework on synthetic and real-world datasets to discover the identity-location-time (a.k.a who-where-when) patterns. The experimental results highlight the effectiveness of our MCNC framework in both cases of complete and missing data.