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Clustering by Regression Analysis

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Data Warehousing and Knowledge Discovery (DaWaK 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2737))

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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 avoid this deficiency. Here we assume there exists aspects of local regression in data. Then we develop our theory to combine clusters using \(\mathcal{F}\) values by regression analysis as criterion. We examine experiments and show how well the theory works.

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References

  1. Bezdek, J.C.: Numerical taxonomy with fuzzy sets. Journal of Mathematical Biology 1, 57–71 (1974)

    Article  MATH  MathSciNet  Google Scholar 

  2. Chakrabarti, K., Mehrotra, S.: Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces. In: Proc.VLDB (2000)

    Google Scholar 

  3. Cheeseman, P., et al.: Bayesian classification. In: Proc. ACM Artificial Intelligence, pp. 607–611 (1988)

    Google Scholar 

  4. Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clutering – A Review. ACM Computing Surveys 31(3), 264–323 (1999)

    Article  Google Scholar 

  5. Japan Weather Association: Weather Data HIMAWARI, Maruzen (1998)

    Google Scholar 

  6. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc. Fifth Berkeley Symposium observations, ProStatistics and Probability, vol. 1. University of California Press, Berkeley (1967)

    Google Scholar 

  7. Motoyoshi, M., Miura, T., Watanabe, K., Shioya, I.: Mining Temporal Classes from Time Series Data. In: Proc.ACM Conf. on Information and Knowledge Management, CIKM (2002)

    Google Scholar 

  8. Wallace, C.S., Dowe, D.L.: Intrinsic classification by MML-the Snob program. In: Proc. 7th Australian Joint Conference on Artificial Intelligence, pp. 37–44 (1994)

    Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Motoyoshi, M., Miura, T., Shioya, I. (2003). Clustering by Regression Analysis. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2003. Lecture Notes in Computer Science, vol 2737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45228-7_21

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  • DOI: https://doi.org/10.1007/978-3-540-45228-7_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40807-9

  • Online ISBN: 978-3-540-45228-7

  • eBook Packages: Springer Book Archive

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