MCNC: multi-channel nonparametric clustering from heterogeneous data

Thanh Binh Nguyen, Vu Nguyen, Svetha Venkatesh, Dinh Phung

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4 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR 2016)
EditorsLarry Davis , Alberto Del Bimbo, Brian C. Lovell
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781509048472
ISBN (Print)9781509048489
Publication statusPublished - 2016
Externally publishedYes
EventInternational Conference on Pattern Recognition 2016 - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016
Conference number: 23rd (Proceedings)


ConferenceInternational Conference on Pattern Recognition 2016
Abbreviated titleICPR 2016
Internet address


  • Bayesian nonparametrics
  • Clustering
  • Heterogeneous data
  • MCNC
  • Product-space
  • Ubiquitous computing

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