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Rearranging data objects for efficient and stable clustering

Published: 13 March 2005 Publication History

Abstract

When a partitional structure is derived from a data set using a data mining algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. To overcome this problem, the first clustering process proceeds to construct an initial partition. The partition is expected to imply the possible range in the number of final clusters. We apply center sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm, REIT, that achieves both efficiency and reliability. A number of experiments have been performed to show that the algorithm helps minimize the order bias effects.

References

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Talavera, L., Dependency-based feature selection for clustering symbolic data, Intelligent Data Analysis, vol. 4, 2000. 19--28.
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Fisher, D., Iterative Optimization and Simplification of Hierarchical Clustering. Journal of AI Research, 4, 1996. 147--179.
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Biswas, G, Weinberg, J. B. and Fisher, H. D. ITERATE: A conceptual clustering algorithm for data mining, IEEE Tr. on Systems, Man and Cybernetics, Vol. 28, Part C No. 2. 1998.
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Fisher, D. H., Knowledge acquisition via incremental conceptual clustering, Machine Learning 2, 1987. 139--172
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Li, C. and Biswas, G, "A Bayesian Approach for Learning Hidden Markov Models from Data", in the special issue on Markov Chain and Hidden Markov Models, Scientific Programming, Volume 10, Number 3, 2002. 201--219.
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Blake, C. L. and Merz, C. J. UCI Repository of Machine Learning Databases {http://www.ics.uci.edu/~mlearn/MLRepository.html}. Irvine, CA: University of California, Department of Information and Computer Science, 1998.

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  1. Rearranging data objects for efficient and stable clustering

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    cover image ACM Conferences
    SAC '05: Proceedings of the 2005 ACM symposium on Applied computing
    March 2005
    1814 pages
    ISBN:1581139640
    DOI:10.1145/1066677
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    Publication History

    Published: 13 March 2005

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    Author Tags

    1. bias
    2. data ordering
    3. hierarchical clustering

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    SAC05
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    SAC05: The 2005 ACM Symposium on Applied Computing
    March 13 - 17, 2005
    New Mexico, Santa Fe

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    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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