dc.contributor.authorFan, Xuhui
dc.contributor.authorZhu, Lin
dc.contributor.authorCao, Longbing
dc.contributor.authorCui, Xia
dc.contributor.authorOng, Yew Soon
dc.identifier.citationFan, X., Zhu, L., Cao, L., Cui, X., & Ong, Y.-S. (2012). Maximum margin clustering on evolutionary data. Proceedings of the 21st ACM international conference on Information and knowledge management.en_US
dc.description.abstractEvolutionary data, such as topic changing blogs and evolving trading behaviors in capital market, is widely seen in business and social applications. The time factor and intrinsic change embedded in evolutionary data greatly challenge evolutionary clustering. To incorporate the time factor, existing methods mainly regard the evolutionary clustering problem as a linear combination of snapshot cost and temporal cost, and reflect the time factor through the temporal cost. It still faces accuracy and scalability challenge though promising results gotten. This paper proposes a novel evolutionary clustering approach, evolutionary maximum margin clustering (e-MMC), to cluster large-scale evolutionary data from the maximum margin perspective. e-MMC incorporates two frameworks: Data Integration from the data changing perspective and Model Integration corresponding to model adjustment to tackle the time factor and change, with an adaptive label allocation mechanism. Three e-MMC clustering algorithms are proposed based on the two frameworks. Extensive experiments are performed on synthetic data, UCI data and real-world blog data, which confirm that e-MMC outperforms the state-of-the-art clustering algorithms in terms of accuracy, computational cost and scalability. It shows that e-MMC is particularly suitable for clustering large-scale evolving data.en_US
dc.rights© 2012 ACM.en_US
dc.titleMaximum margin clustering on evolutionary dataen_US
dc.typeConference Paper
dc.contributor.conferenceInternational conference on Information and knowledge management (21st : 2012 : Maui, USA)en_US
dc.contributor.schoolSchool of Computer Engineeringen_US

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