A Text Clustering Algorithms Based on Hidden Markov Model

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Based on the probability model of clustering algorithm constructs a model for each cluster, calculate probability of every text falls in different models to decide text belongs to which cluster, conveniently in global Angle represents abstract structure of clusters. In this paper combining the hidden Markov model and k - means clustering algorithm realize text clustering, first produces first clustering results by k - means algorithm, as the initial probability model of a hidden Markov model ,constructed probability transfer matrix prediction every step of clustering iteration, when subtraction value of two probability transfer matrix is 0, clustering end. This algorithm can in global perspective every cluster of document clustering process, to avoid the repetition of clustering process, effectively improve the clustering algorithm .

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Periodical:

Edited by:

Robin G. Qiu and Yongfeng Ju

Pages:

1155-1158

DOI:

10.4028/www.scientific.net/AMM.135-136.1155

Citation:

W. Li and M. A. Li, "A Text Clustering Algorithms Based on Hidden Markov Model", Applied Mechanics and Materials, Vols. 135-136, pp. 1155-1158, 2012

Online since:

October 2011

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$35.00

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