Application of Algorithm CARDBK in Document Clustering

作     者：ZHU Yehang ZHANG Mingjie SHI Feng 无

作者机构：College of Economics and Management Xi'an University of Posts and Telecommunications Xi'an 710121 Shaanxi China Information Business Department Puyang Technician College Puyang 457000 Henan China 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2018年第23卷第6期

页      面：514-524页

核心收录：

学科分类：08[工学] 081203[工学-计算机应用技术] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the Social Science Foundation of Shaanxi Province of China(2018P03) the Humanities and Social Sciences Research Youth Fund Project of Ministry of Education of China(13YJCZH251) 

主　　题：algorithm design and analysis clustering documentanalysis text processing 

摘      要：In the K-means clustering algorithm, each data point is uniquely placed into one category. The clustering quality is heavily dependent on the initial cluster centroid. Different initializations can yield varied results; local adjustment cannot save the clustering result from poor local optima. If there is an anomaly in a cluster, it will seriously affect the cluster mean value. The K-means clustering algorithm is only suitable for clusters with convex shapes. We therefore propose a novel clustering algorithm CARDBK—＂centroid all rank distance（CARD）＂ which means that all centroids are sorted by distance value from one point and ＂BK＂ are the initials of ＂batch K-means＂—in which one point not only modifies a cluster centroid nearest to this point but also modifies multiple clusters centroids adjacent to this point, and the degree of influence of a point on a cluster centroid depends on the distance value between this point and the other nearer cluster centroids. Experimental results showed that our CARDBK algorithm outperformed other algorithms when tested on a number of different data sets based on the following performance indexes： entropy, purity, F1 value, Rand index and normalized mutual information（NMI）. Our algorithm manifested to be more stable, linearly scalable and faster.