Enhancing the usage pattern mining performance with temporal segmentation of QPop Increment in digital libraries

作     者：曹三省 KLEIN R. Rody 刘剑波 

作者机构：Information Engineering School Communication University of China RCID Zhejiang University 

出 版 物：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 (浙江大学学报（英文版）A辑（应用物理与工程）)

年 卷 期：2005年第6卷第11期

页      面：1290-1296页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 12[管理学] 120501[管理学-图书馆学] 120502[管理学-情报学] 

主　　题：Media-rich Digital library Data migration Media content Log mining QPop 

摘      要：The convergence of next-generation Networks and the emergence of new media systems have made media-rich digital libraries popular in application and research. The discovery of media content objects’ usage patterns, where QPop Increment is the characteristic feature under study, is the basis of intelligent data migration scheduling, the very key issue for these systems to manage effectively the massive storage facilities in their backbones. In this paper, a clustering algorithm is established, on the basis of temporal segmentation of QPop Increment, so as to improve the mining performance. We employed the standard C-Means algorithm as the clustering kernel, and carried out the experimental mining process with segmented QPop Increases obtained in actual applications. The results indicated that the improved algorithm is more advantageous than the basic one in important indices such as the clustering cohesion. The experimental study in this paper is based on a Media Assets Library prototype developed for the use of the advertainment movie production project for Olympics 2008, under the support of both the Humanistic Olympics Study Center in Beijing, and China State Administration of Radio, Film and TV.