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Temporal Multinomial Mixture for Instance-Oriented Evolutionary Clustering

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 9022)

Abstract

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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Kim, YM., Velcin, J., Bonnevay, S., Rizoiu, MA. (2015). Temporal Multinomial Mixture for Instance-Oriented Evolutionary Clustering. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_66

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  • DOI: https://doi.org/10.1007/978-3-319-16354-3_66

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16353-6

  • Online ISBN: 978-3-319-16354-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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