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The traditional K-means algorithm is sensitive to the initial points and easy to fall into local optimum. To avoid this kind of flaw, an improved GA-based text clustering algorithm CGHCM is proposed. The new algorithm is proven effective to avoid falling into local optimum and obtains better clustering results.
The traditional K-means algorithm is sensitive to the initial point, easy to fall into local optimum. In order to avoid this kind of flaw, an improved K-means text clustering method WIKTCM is proposed. The new method creates an innovative initial centers selection method and accommodates the contribution of characteristics of different parts of speech to the text. In addition, the impact of outliers...
Text classification has gained booming interest over the past few years. As a simple, effective and nonparametric classification method, KNN method is widely used in document classification. However, the uneven distribution in training set will affect the KNN classified result negatively. Moreover, the uneven distribution phenomenon of text is very common in documents on the Web. To tackling on this,...
Text classification has gained booming interest over the past few years. The traditional approaches of text classification commonly extract features from a signal test criterion, resulting in the problem of “over fitting”. This paper takes test criterions such as frequency, dispersion and concentration indices into account and proposes an improved dimension reduction method and feature weighting method,...
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