Refining Initial Points for K-Means Clustering

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Refining Initial Points for K-Means Clustering (1998)

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by P. S. Bradley , Usama M. Fayyad

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316 - 5 self

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@INPROCEEDINGS{Bradley98refininginitial,
    author = {P. S. Bradley and Usama M. Fayyad},
    title = {Refining Initial Points for K-Means Clustering},
    booktitle = {},
    year = {1998},
    pages = {91--99},
    publisher = {Morgan kaufmann}
}

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Abstract

Practical approaches to clustering use an iterative procedure (e.g. K-Means, EM) which converges to one of numerous local minima. It is known that these iterative techniques are especially sensitive to initial starting conditions. We present a procedure for computing a refined starting condition from a given initial one that is based on an efficient technique for estimating the modes of a distribution. The refined initial starting condition allows the iterative algorithm to converge to a "better" local minimum. The procedure is applicable to a wide class of clustering algorithms for both discrete and continuous data. We demonstrate the application of this method to the popular K-Means clustering algorithm and show that refined initial starting points indeed lead to improved solutions. Refinement run time is considerably lower than the time required to cluster the full database. The method is scalable and can be coupled with a scalable clustering algorithm to address the large-scale cl...

Keyphrases

k-means clustering initial point full database wide class improved solution continuous data iterative technique local minimum practical approach large-scale cl iterative procedure numerous local minimum iterative algorithm efficient technique popular k-means refinement run time scalable clustering algorithm

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