Fast and high-quality text clustering algorithm is an important and challenging problem in effectively navigating. Such as the high-dimensional sparse text data, poor efficiency of unsupervised feature selection, and ...
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ISBN:
(纸本)9781424452729
Fast and high-quality text clustering algorithm is an important and challenging problem in effectively navigating. Such as the high-dimensional sparse text data, poor efficiency of unsupervised feature selection, anddefects existing in classical clustering methods and so on. In this paper, an effective and unsupervised text clustering method (OK-PSO) is proposed. First, k-means is used to calculate the distance from each term to the cluster centers, and then the two-dimensional otsualgorithm is included to evaluate the optimization of clustering distance threshold. The process of 2dotsu is taken by PSO algorithm. Finally, several experiments are taken based on OK-PSO and some other methods. The experimental results illustrate the efficiency of OK-PSO method proposed in this paper.
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