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The discovery of user related rare sequential patterns of topics in the internet document stream

Published: 24 March 2014 Publication History

Abstract

On the Internet, plain text documents created and viewed by users constitute ever changing document streams. Lots of the literature is devoted to topic modeling, while the sequential patterns of topics in document streams are ignored. In this paper, we deal with the problem of mining user related rare sequential patterns of topics in the Internet document streams, which can be used in many fields, such as real-time user behavioral monitoring on the Internet. We propose an approach to discover rare patterns based on the temporal and probabilistic information of topics. Experiments show that the proposed approach can discover user related rare patterns of topic effectively.

References

[1]
D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3: 993--1022, 2003.
[2]
D. M. Blei and J. D. Lafferty. Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning, pages 113--120, New York, NY, USA, 2006. ACM.
[3]
A. K. McCallum. Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu, 2002.
[4]
C. H. Mooney and J. F. Roddick. Sequential pattern mining -- approaches and algorithms. ACM Comput. Surv., 45(2): 19:1--19:39, 2013.
[5]
X. Wang and A. McCallum. Topics over time: a non-markov continuous-time model of topical trends. In KDD, pages 424--433. ACM, 2006.

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  1. The discovery of user related rare sequential patterns of topics in the internet document stream

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    cover image ACM Conferences
    SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
    March 2014
    1890 pages
    ISBN:9781450324694
    DOI:10.1145/2554850
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    Publication History

    Published: 24 March 2014

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    Author Tags

    1. data mining
    2. data stream
    3. rare event
    4. topic pattern

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    SAC 2014
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    SAC 2014: Symposium on Applied Computing
    March 24 - 28, 2014
    Gyeongju, Republic of Korea

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    SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    The 40th ACM/SIGAPP Symposium on Applied Computing
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