Article

Dynamic mixture models for multiple time series

Authors:

Xing Wei,

Jimeng Sun,

Xuerui WangAuthors Info & Claims

IJCAI'07: Proceedings of the 20th international joint conference on Artifical intelligence

Pages 2909 - 2914

Published: 06 January 2007 Publication History

Abstract

Traditional probabilistic mixture models such as Latent Dirichlet Allocation imply that data records (such as documents) are fully exchangeable. However, data are naturally collected along time, thus obey some order in time. In this paper, we present Dynamic Mixture Models (DMMs) for online pattern discovery in multiple time series. DMMs do not have the noticeable drawback of the SVD-based methods for data streams: negative values in hidden variables are often produced even with all non-negative inputs. We apply DMM models to two real-world datasets, and achieve significantly better results with intuitive interpretation.

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Cited By

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Liang S(2019)Collaborative, dynamic and diversified user profilingProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33014269(4269-4276)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33014269
Liang SYilmaz EKanoulas E(2019)Collaboratively Tracking Interests for User Clustering in Streams of Short TextsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.283221131:2(257-272)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1109/TKDE.2018.2832211
Liang SMcIlraith SWeinberger K(2018)Dynamic user profiling for streams of short textsProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence10.5555/3504035.3504754(5860-5867)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.5555/3504035.3504754
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Information

Published In

IJCAI'07: Proceedings of the 20th international joint conference on Artifical intelligence

January 2007

2953 pages

Editors:
Rajeev Sangal
International Institute of Information Technology, Hyderabad
,
Harish Mehta
Onward Technologies Limited
,
R. K. Bagga
International Institute of Information Technology, Hyderabad

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

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Published: 06 January 2007

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Liang S(2019)Collaborative, dynamic and diversified user profilingProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33014269(4269-4276)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33014269
Liang SYilmaz EKanoulas E(2019)Collaboratively Tracking Interests for User Clustering in Streams of Short TextsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.283221131:2(257-272)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1109/TKDE.2018.2832211
Liang SMcIlraith SWeinberger K(2018)Dynamic user profiling for streams of short textsProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence10.5555/3504035.3504754(5860-5867)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.5555/3504035.3504754
Yin JChao DLiu ZZhang WYu XWang JGuo YFarooq F(2018)Model-based Clustering of Short Text StreamsProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3220094(2634-2642)Online publication date: 19-Jul-2018
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Liang SZhang XRen ZKanoulas EGuo YFarooq F(2018)Dynamic Embeddings for User Profiling in TwitterProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3220043(1764-1773)Online publication date: 19-Jul-2018
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Liang SRen ZYilmaz EKanoulas ESingh SMarkovitch S(2017)Collaborative user clustering for short text streamsProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298023.3298077(3504-3510)Online publication date: 4-Feb-2017
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Guo JGong Z(2017)A density-based nonparametric model for online event discovery from the social media dataProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172128(1732-1738)Online publication date: 19-Aug-2017
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Liang SRen ZZhao YMa JYilmaz ERijke M(2017)Inferring Dynamic User Interests in Streams of Short Texts for User ClusteringACM Transactions on Information Systems10.1145/307260636:1(1-37)Online publication date: 17-Jul-2017
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