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Clustering stream data by regression analysis

Published: 01 January 2004 Publication History
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  • Abstract

    In data clustering, many approaches have been proposed such as K-means method and hierarchical method. One of the problems is that the results depend heavily on initial values and criterion to combine clusters. In this investigation, we propose a new method to cluster stream data while avoiding this deficiency. Here we assume there exists aspects of local regression in data. Then we develop our theory to combine clusters using F values by regression analysis as criterion and to adapt to stream data. We examine experiments and show how well the theory works.

    References

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    Chakrabarti, K., & Mehrotra, S. (2000), "Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces", proc. VLDB.
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    Han, J. & Kamber, M. (2000), "Data Mining - Concepts and Techniques", Morgan Kaufmann.
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    Jain, A. K., Murty, M. N. & Flynn, P. J. (1999), "Data Clutering -- A Review", ACM Computing Surveys, Vol. 31--3, pp. 264--323.
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    Japan Weather Association (1998), "Weather Data HIMAWARI", Maruzen.
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    Motoyoshi, M., Miura, T., Watanabe, K. & Shioya, I. (2002), "Mining Temporal Classes from Time Series Data", proc. ACM Conf. on Information and Knowledge Management (CIKM), pp. 493--498.
    [6]
    Motoyoshi, M., Miura, T. & Shioya, I. (2003), "Clustering by Regression Analysis", proc. Conf. on Data Warehousing and Knowledge Discovery (DaWaK), pp. 202--211.

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    Published In

    cover image DL Hosted proceedings
    ACSW Frontiers '04: Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
    January 2004
    192 pages

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    Australian Computer Society, Inc.

    Australia

    Publication History

    Published: 01 January 2004

    Author Tags

    1. clustering for stream
    2. data mining
    3. data stream
    4. regression analysis

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