关键词: K-means算法, 动态聚类, 特征选择, 信息熵

Abstract: By means of a four-mining model which is constructed based on the structural characteristics of scientific literatures, a text feature selection method is proposed to apply in classification of scientific literatures. The proposed method firstly divides scientific literature into four layers according to its structure, and then selects features progressively for the former three layers by K-means algorithm, and finally finds out the maximum frequent itemsets of fourth layer by Aprori algorithm to act as a collection of fourth layer features. Meanwhile, K-means algorithm is also improved which firstly uses information entropy empower the clustering objects to correct the distance function, and then employs empowerment function value to select the optimal initial clustering center, and subsequently reduces algorithm iterations and learning time by setting the standard value for termination condition of the algorithm and reduces interference of dynamic clustering by removing redundant information from the changing information to make the algorithm achieve more accurate and efficient clustering effect. So, it is possible for this proposed method to find features more accurately in the literature corpus. Experimental results show that the proposed method is feasible and effective, and has higher performance in scientific literature classification which is compared with the previous methods.

Key words: K-means algorithm, dynamic clustering, feature selection, information entropy