MCNC: multi-channel nonparametric clustering from heterogeneous data

Nguyen, Thanh Binh, Nguyen, Tien Vu, Venkatesh, Svetha and Phung, Quoc-Dinh 2016, MCNC: multi-channel nonparametric clustering from heterogeneous data, in 2016 23rd International Conference on Pattern Recognition (ICPR 2016), IEEE, Piscataway, N.J., pp. 3633-3638, doi: 10.1109/ICPR.2016.7900198.

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Title MCNC: multi-channel nonparametric clustering from heterogeneous data
Author(s) Nguyen, Thanh BinhORCID iD for Nguyen, Thanh Binh orcid.org/0000-0003-4527-8826
Nguyen, Tien VuORCID iD for Nguyen, Tien Vu orcid.org/0000-0001-8675-6631
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0002-9977-8247
Phung, Quoc-Dinh
Conference name Pattern Recognition. Conference (23rd : 2016 : Cancun, Mexico)
Conference location Cancun, Mexico
Conference dates 2016/12/04 - 2016/12/08
Title of proceedings 2016 23rd International Conference on Pattern Recognition (ICPR 2016)
Editor(s) [Unknown],
Publication date 2016
Start page 3633
End page 3638
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Bayesian nonparametrics
clustering
heterogeneous data
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
MCNC
product-space
ubiquitous computing
MIXTURES
Summary 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.
ISBN 9781509048472
ISSN 1051-4651
Language eng
DOI 10.1109/ICPR.2016.7900198
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, by the Institute of Electrical and Electronics Engineers, Inc
Persistent URL http://hdl.handle.net/10536/DRO/DU:30096921

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