Implication Intensity: Randomized F-measure for Cluster Evaluation

摘要：

The ever-growing resources of information and services on World Wide Web provide a welcome boost for the researches in the information retrieval space. Text clustering groups a set of documents into subsets or clusters so that the vast retrieved documents can be browsed selectively and efficiently. Many cluster validation measures, such as the F-measure, are then introduced to evaluate the clustering qualities. In this paper, however, we demonstrate that this widely adopted F-measure suffers from the so-call increment effect which may mislead the comparison of clustering results with different cluster numbers. To meet this challenge, we propose a novel “implication intensity (IMI) measure based on the F-measure and a random clustering perspective. Experimental results on real-world data sets demonstrate that IMI shows merits on alleviating the increment effect introduced by the F-measure.

关键词： cluster evaluation increment effect F-measure implication intensity

作者: Limin Li Junjie Wu Shiwei Zhu

作者单位: School of Economics and Management,Beihang University,Beijing 100083,China

会议类型: 国际会议

会议名称: 2009 6th International Conference on Service Systems and Service Management( 2009 第六届服务系统与服务管理国际会议)

会议地点: 厦门

会议语种:英文

页码: 510-515

在线出版日期: 2009-06-08（万方平台首次上网日期，不代表论文的发表时间）