2009 International Conference on Information Technology and Computer Science 2009
DOI: 10.1109/itcs.2009.269
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Abstract: By researching all kinds of methods for document clustering, we put forward a new dynamic method based on genetic algorithm (GA). K-means is a greedy algorithm, which is sensitive to the choice of cluster center and very easily results in local optimization. Genetic algorithm is a global convergence algorithm, which can find the best cluster centers easily. Among the traditional document clustering methods, the document similar matrix is a sparse matrix. In this paper, we propose some new formulas improved on … Show more

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Cited by 12 publications
(9 citation statements)
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References 9 publications
(9 reference statements)
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“…Finally, since the objective function is the most distinguished portion of evolutionary algorithm, a summary of all fitness functions adopted for all disciplines is going be discuss in the next section (section 5). Wei et al (2009) put forward a new dynamic method based on GA for document clustering. The method established on a new formula for describing the similarities of Chinese text documents.…”
Section: Fig 5 Main Researches' Disciplines In Document Clustering mentioning
confidence: 99%
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“…Finally, since the objective function is the most distinguished portion of evolutionary algorithm, a summary of all fitness functions adopted for all disciplines is going be discuss in the next section (section 5). Wei et al (2009) put forward a new dynamic method based on GA for document clustering. The method established on a new formula for describing the similarities of Chinese text documents.…”
Section: Fig 5 Main Researches' Disciplines In Document Clustering mentioning
confidence: 99%
“…Additionally, most of these researches dealt with the problem as a maximization problem, except in (Wei et al, 2009) and 2010;2012) because the intraclustering and BIC are minimization in its nature. While in and Lee et al, 2011;Lee and Park, 2012;Song and Park, 2006;2007a;2007b), the researchers adopted the inverse of the DB index to convert the problem into a maximization problem.…”
Section: The Objective Functions Used In Document Clusteringmentioning
confidence: 99%
“…E.g. if two chromosomes of 5 centers are (1,4,6,7,9) and (5,11,10,8,1). First find out common center i.e.…”
Section: Crossover Operator Of Ga and Ddementioning
confidence: 99%
“…E.g. if one chromosome of 5 centers is (1,4,6,7,9) and we want to update second gene's value, so we will replace 4 by value v ϵ {1, 2, … , n}-{1, 4, 6, 7, 9}.…”
Section: Mutation Operator Of Ga and Ddementioning
confidence: 99%
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