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《Journal of Intelligence》 2015-02
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Two-stage Resources Clustering in Social Tagging System Based on Improved K-medoids

Guo Weiguang;Wang Benqiang;Yang Xuechun;Department of Management,Hefei University;  
In allusion to the disadvantages such as semantic fuzziness of social tags,sensitive to the initial selection of the center and slow convergent speed,only partition the object into sole classification in traditional K-medoids clustering algorithm,this paper designed a two-stage resources clustering algorithm based on improved K-medoids in social tagging system.This algorithm is used to cluster the resources by using a new simple and efficient selective rule of the initial cluster centers and improved clustering objective function,which performs tags clustering firstly,searches resources through tags,then partitions the resources into several categories,and then performs resources clustering of each category.Experiment proves that the proposed algorithm can automatically and reasonably identify the initial clustering centers and has fast convergent speed and better accuracy.
【Fund】: 安徽省教育厅自然科学项目“基于社会化标签的用户兴趣建模与个性化信息推荐研究”(编号:KJ2012B155);; 合肥学院科研发展基金重点项目“基于社会化标注的电子商务商品个性化推荐模型及算法研究”(编号:13KY08ZD);; 合肥学院学科带头人培养对象项目(编号:2014dtr04)
【CateGory Index】: TP311.13
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