Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Genetic algorithm for text clustering based on latent semantic indexing
Computers & Mathematics with Applications
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A modified variable string length genetic algorithm, called MVGA, is proposed for text clustering in this paper. Our algorithm has been exploited for automatically evolving the optimal number of clusters as well as providing proper data set clustering. The chromosome is encoded by special indices to indicate the location of each gene. More effective version of evolutional steps can automatically adjust the influence between the diversity of the population and selective pressure during generations. The superiority of the MVGA over conventional variable string length genetic algorithm (VGA) is demonstrated by providing proper text clustering.