Shandong Institute of Business and Technology, Yantai ,China

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Abstract—Text clustering is one of the key research areas in data mining. K-medoids is a classical partitioning algorithm, which can better solve the isolated point problem, but it often converges to local optimization. In this paper, we put forward a new genetic algorithm called KGA algorithm by putting k-medoids into the genetic algorithm, then we form a local Optimal Solution with multiple initial species group, strategy for crossover within a species group and crossover among species groups, using the mutation threshold to control mutation. This algorithm will increase the diversity of species group and enhance the optimization capability of genetic algorithm, thus improve the accuracy of clustering and the capacity of acquiring isolated points.

Index Terms—Text clustering, K-medoids algorithm, genetic algorithm

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Cite: ZhanGang Hao, "A New Text Clustering Method Based on KGA," Journal of Software vol. 7, no. 5, pp. 1094-1098, 2012.