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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 is considered. Experimental results show that the new method has better clustering results.