文章基本信息

标题：OPTIMAL INITIAL CENTROID IN K-MEANS FOR CRIME TOPIC
本地全文：下载
作者：MASNIZAH MOHD ; QUSAY WALID BSOUL ; NAZLENA MOHAMAD ALI 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2012
卷号：45
期号：1
页码：019-026
出版社：Journal of Theoretical and Applied
摘要：A wide number of different clustering method applications and their effectiveness in crime topics have been examined in this paper. Several works have investigated the optimal initial centroid of clustering crime topics. In this paper, wehave compared the effectiveness of single pass clustering and k-means in detecting crime topics and aiding in the identification of events or crimes. We have also experimentedon enhanced k-means clustering, in order to select the optimal initial centroid to be automatically compared with regular k-means, to choose the initial centroid randomly. Based on the main findings of this study, it was revealed that the experimental method, which was based on k-means, was proved to be better and more effective than single pass clustering in detecting and identifying events or crimes. For the initial number of centroids, it was found that the proposed method was more effective when used in selecting terms that were more than the number of topics, than when they were less. However, the best result was obtained when choosing a number of topics equal to the number of original topics. This implies that the optimal accuracy of clustering is achieved when selecting a large number of documents that have terms better than randomly chosen documents as a centroid.
关键词：Crime Clustering; Single Pass; K-Means; Crime Topic