A divisive information theoretic feature clustering algorithm for text classification
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International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers of KES2012-Part 2 of 2
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The k-means algorithm with cosine similarity, alsoknown as the spherical k-means algorithm, is a popularmethod for clustering document collections. However,spherical k-means can often yield qualitatively poor results,especially when cluster sizes are small, say 25-30 documentsper cluster, where it tends to get stuck at a localmaximum far away from the optimal solution. In this paper,we present a local search procedure, which we call"first-variation" that refines a given clustering by incrementallymoving data points between clusters, thus achievinga higher objective function value. An enhancement offirst variation allows a chain of such moves in a Kernighan-Linfashion and leads to a better local maximum. Combiningthe enhanced first-variation with spherical k-meansyields a powerful "ping-pong" strategy that often qualitativelyimproves k-means clustering and is computationallyefficient. We present several experimental results to high-lightthe improvement achieved by our proposed algorithmin clustering high-dimensional and sparse text data.