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Novel Approaches to Unsupervised Clustering Through k-Windows Algorithm

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Knowledge Mining

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 185))

Summary

The extraction of meaningful information from large collections of data is a fundamental issues in science. To this end, clustering algorithms are typically employed to identify groups (clusters) of similar objects. A critical issue for any clustering algorithm is the determination of the number of clusters present in a dataset. In this contribution we present a clustering algorithm that in addition to partitioning the data into clusters, it approximates the number of clusters during its execution. We further present modifications of this algorithm for different distributed environments, and dynamic databases. Finally, we present a modification of the algorithm that exploits the fractal dimension of the data to partition the dataset.

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Author information

Authors and Affiliations

  1. Computational Intelligence Laboratory, Department of Mathematics, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, GR-26110, Patras, Greece

    D. K. Tasoulis & M. N. Vrahatis

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  1. D. K. Tasoulis
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  2. M. N. Vrahatis
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Editors and Affiliations

  1. Research Academic Computer Technology, Institute 61 Riga Feraiou Str., 26221, Patras, Greece

    Spiros Sirmakessis

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Tasoulis, D.K., Vrahatis, M.N. (2005). Novel Approaches to Unsupervised Clustering Through k-Windows Algorithm. In: Sirmakessis, S. (eds) Knowledge Mining. Studies in Fuzziness and Soft Computing, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32394-5_5

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  • DOI: https://doi.org/10.1007/3-540-32394-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25070-8

  • Online ISBN: 978-3-540-32394-5

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