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Feature Selection with β-Hill Climbing Search for Text Clustering Application
L.M. Abualigah, A.T. Khader, , Z.A.A. Alyasseri, O.A. Alomari, E.S. Hanandeh
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 22 - 27
In the bases of increasing the volume of text information, the dealing with text information has become incredibly complicated. The text clustering is a suitable technique used in dealing with a tremendous amount of text documents by classifying these set of text documents into clusters. Ultimately, text documents hold sparse, non-uniform distribution and uninformative features are difficult to cluster. The text feature selection is a primary unsupervised learning method that is utilized to choose a new subset of informational text features. In this paper, a new algorithm is proposed based on β-hill climbing technique for text feature selection problem to improve the text clustering (B-FSTC). The results of the proposed method for β-hill climbing and original Hill climbing (i.e., H-FSTC) are examined using the k-mean text clustering and compared with each other. Experiments were conducted on four standard text datasets with varying characteristics. Interestingly, the proposed β-hill climbing algorithm obtains superior results in comparison with the other well-regard techniques by producing a new subset of informational text features. Lastly, the β-hill climbing-based feature selection method supports the k-mean clustering algorithm to achieve more precise clusters. © 2017 IEEE.