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Incremental Clustering Algorithm by Modulating Vigilance Parameter Dynamically  

신광철 (중앙대학교 컴퓨터공학부)
한상용 (중앙대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.30, no.11, 2003 , pp. 1072-1079 More about this Journal
Abstract
This study is purported for suggesting a new clustering algorithm that enables incremental categorization of numerous documents. The suggested algorithm adopts the natures of the spherical k-means algorithm, which clusters a mass amount of high-dimensional documents, and the fuzzy ART(adaptive resonance theory) neural network, which performs clustering incrementally. In short, the suggested algorithm is a combination of the spherical k-means vector space model and concept vector and fuzzy ART vigilance parameter. The new algorithm not only supports incremental clustering and automatically sets the appropriate number of clusters, but also solves the current problems of overfitting caused by outlier and noise. Additionally, concerning the objective function value, which measures the cluster's coherence that is used to evaluate the quality of produced clusters, tests on the CLASSIC3 data set showed that the newly suggested algorithm works better than the spherical k-means by 8.04% in average.
Keywords
spherical k-means; spherical k-means; vector space model; concept vector; fuzzy ART; vigilance parameter;
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Times Cited By KSCI : 1  (Citation Analysis)
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