Temporal multinomial mixture for instance-oriented evolutionary clustering

Kim, YM; Velcin, J; Bonnevay, S; Rizoiu, MA

Temporal multinomial mixture for instance-oriented evolutionary clustering

Kim, YM Velcin, J Bonnevay, S Rizoiu, MA

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 9022 pp. 593 - 604
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Kim, YM	en_US
dc.contributor.author	Velcin, J	en_US
dc.contributor.author	Bonnevay, S	en_US
dc.contributor.author	Rizoiu, MA https://orcid.org/0000-0003-0381-669X	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 9022 pp. 593 - 604	en_US
dc.identifier.isbn	9783319163536	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132690
dc.description.abstract	© Springer International Publishing Switzerland 2015. Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM) is an extension of classical mixture model that optimizes feature co-occurrences in the trade-off with temporal smoothness. Our model is evaluated for two recent case studies on opinion aggregation over time. We compare four different probabilistic clustering models and we show the superiority of our proposal in the task of instance-oriented clustering.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Temporal multinomial mixture for instance-oriented evolutionary clustering	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9022	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	9022	en_US

Abstract:

© Springer International Publishing Switzerland 2015. Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM) is an extension of classical mixture model that optimizes feature co-occurrences in the trade-off with temporal smoothness. Our model is evaluated for two recent case studies on opinion aggregation over time. We compare four different probabilistic clustering models and we show the superiority of our proposal in the task of instance-oriented clustering.

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http://hdl.handle.net/10453/132690