Initialization of iterative refinement clustering algorithms

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Initialization of iterative refinement clustering algorithms (1998)

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by Usama Fayyad , Cory Reina , P. S. Bradley

Venue:	In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD–98
Citations:	75 - 2 self

BibTeX

@INPROCEEDINGS{Fayyad98initializationof,
    author = {Usama Fayyad and Cory Reina and P. S. Bradley},
    title = {Initialization of iterative refinement clustering algorithms},
    booktitle = {In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD–98},
    year = {1998},
    pages = {194--198},
    publisher = {AAAI Press}
}

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Abstract

Iterative refinement clustering algorithms (e.g. K-Means, EM) converge to one of numerous local minima. It is known that they are especially sensitive to initial 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 leads to convergence to “better ” local minima. 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 Expectation Maximization (EM) clustering algorithm and show that refined initial 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 clustering in data mining. 1

Keyphrases

iterative refinement efficient technique full database wide class continuous data initial point local minimum expectation maximization improved solution initial condition numerous local minimum data mining large-scale clustering refinement run time scalable clustering algorithm

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