A Text Categorization Method Based on SVM and Improved K-Means

Abstract:

Traditional supervised classification method such as support vector machine (SVM) could achieve high performance in text categorization. However, we should first hand-labeled the samples before classifying. Its a time-consuming task. Unsupervised method such as k-means could also be used for handling the text categorization problem. However, Traditional k-means could easily be affected by several isolated observations. In this paper, we proposed a new text categorization method. First we improved the traditional k-means clustering algorithm. The improved k-means is used for clustering vectors in our vector space model. After that, we use the SVM to categorize vectors which are preprocessed by improved k-means. The experiments show that our algorithm could out-perform the traditional SVM text categorization method.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 2449-2453
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.2449
• Online since: September 2013
• Authors: Rong Ze Xia*, Yan Jia, Hu Li
• Keywords: K-Mean, Support Vector Machine (SVM), Text Categorization

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