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New Technology of Library and Information Service  2013, Vol. 29 Issue (2): 50-56    DOI: 10.11925/infotech.1003-3513.2013.02.08
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Web Usage Mining Using Reduction of Knowledge Granule
Zhao Jie, Mo Zan, Liu Hongwei, Zhang Shaqing, Dong Zhenning
School of Management, Guangdong University of Technology, Guangzhou 510520, China
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Abstract  This paper proposes multi-granularity Web user behavior description model using granular theory, then the reduction algorithm based on knowledge granule is applied for the data. The experiment results prove that the model can not only descript multi-granularity user behavior characteristics, but also have the effect of horizontal dimension reduction. And efficient vertical dimension reduction is achieved by the reduction algorithm, which effectively reduce the work in the subsequent pattern analysis.
Key wordsWeb usage mining      Multi-granularity      Reduction     
Received: 26 December 2012      Published: 24 April 2013
:  TP393  

Cite this article:

Zhao Jie, Mo Zan, Liu Hongwei, Zhang Shaqing, Dong Zhenning. Web Usage Mining Using Reduction of Knowledge Granule. New Technology of Library and Information Service, 2013, 29(2): 50-56.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2013.02.08     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2013/V29/I2/50

[1] Nikulin V, McLachlan G J. Merging Algorithm to Reduce Dimensionality in Application to Web-Mining[C]. In: Proceedings of the 20th Australian Joint Conference on Advances in Artificial Intelligence(AI'07). Berlin, Heidelberg: Springer, 2007: 755-761.
[2] Hosseini M, Abolhassani H. Hierarchical Co-clustering for Web Queries and Selected URLs[C]. In: Proceedings of the 8th International Conference on Web Information Systems Engineering(WISE'07). Berlin, Heidelberg: Springer, 2007: 653-662.
[3] Song A B, Zhao M X, Liang Z P, et al. Discovering User Profiles for Web Personalized Recommendation[J]. Journal of Computer Science and Technology, 2004, 19(3): 320-328.
[4] 吴萍,宋瀚涛,牛振东,等. 基于 SS/OSF 实现高维稀疏数据对象的聚类[J]. 北京理工大学学报, 2006, 26(3): 216-220. (Wu Ping, Song Hantao, Niu Zhendong, et al. SS/OSF for High-Dimensional Sparse Data Object Clustering[J].Transactions of Beijing Institute of Technology, 2006, 26(3): 216-220.)
[5] Bedi P, Chawla S. Use of Fuzzy Rough Set Attribute Reduction in High Scent Web Page Recommendations[C]. In: Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing(RSFDGrC'09). Berlin, Heidelberg: Springer, 2009: 192-200.
[6] 许建潮. Web 挖掘中若干问题的研究[D]. 长春:吉林大学, 2005.(Xu Jianchao. Some Studies for Web Mining[D]. Changchun: Jilin University, 2005. )
[7] Zadeh L A. Some Reflections on Soft Computing, Granular Computing and Their Roles in the Conception, Design and Utilization of Information/Intelligent Systems[J]. Soft Computing, 1998, 2(1): 23-25.
[8] 苗夺谦,王国胤,刘清,等. 粒计算: 过去, 现在与展望[M]. 北京:科学出版社, 2007.(Miao Duoqian, Wang Guoyin, Liu Qing, et al. Granular Computing: Past, Present and Prospects[M]. Beijing: Science Press, 2007.)
[9] 安秋生,沈钧毅,王国胤. 基于信息粒度与 Rough 集的聚类方法研究[J]. 模式识别与人工智能, 2003, 16(4): 412-417.(An Qiusheng, Shen Junyi, Wang Guoyin. A Clustering Method Based on Information Granularity and Rough Sets[J].Pattern Recognition and Artificial Intelligence, 2003, 16(4): 412-417.)
[10] 赵洁,董振宁,张沙清,等. 一种基于粒度原理的多指标综合 Web 用户聚类算法[J]. 计算机应用研究, 2011, 28(7): 2427-2431.(Zhao Jie, Dong Zhenning, Zhang Shaqing, et al. Granular Principle Based Multi-index Synthetical Web User Clustering Algorithm[J]. Application Research of Computers, 2011, 28(7):2427-2431.)
[11] 刘少辉,盛秋戬,吴斌,等. Rough 集高效算法的研究[J]. 计算机学报, 2003, 26(5): 524-529. (Liu Shaohui, Sheng Qiujian, Wu Bin, et al .Research on Efficient Algorithms for Rough Set Methods[J]. Chinese Journal of Computers,2003, 26(5): 524-529.)
[12] 赵洁,肖南峰. 一种基于知识颗粒的高效完备属性约简算法[J]. 中南大学学报:自然科学版, 2009, 40(6): 1623-1629.(Zhao Jie, Xiao Nanfeng. An Efficient and Complete Attribute Reduction Algorithm Based on Knowledge Granular[J]. Journal of Central South University Science and Technology,2009, 40(6): 1623-1629.)
[13] 宋江春,沈钧毅. 一种新的 Web 用户群体和 URL 聚类算法的研究[J]. 控制与决策, 2007, 22(3): 284-288.(Song Jiangchun, Shen Junyi. Research on a New Clustering Algorithm of Web User Communities and Web Site's URLs [J].Control and Decision, 2007, 22(3): 284-288.)
[14] 陈建斌. 高维聚类知识发现关键技术研究及应用[M]. 北京:电子工业出版社, 2009.(Chen Jianbin. Research and Application of Key Technologies in High-dimensional Clustering Knowledge Discovery[M]. Beijing: Publishing House of Electronics Industry,2009.)
[15] 赵亚琴,周献中,何新,等. 一种有效的高属性维稀疏数据聚类算法[J]. 模式识别与人工智能,2006, 19(3): 289-294.(Zhao Yaqin, Zhou Xianzhong, He Xin, et al. An Effective High Attribute Dimensional Sparse Clustering[J]. Pattern Recognition and Artificial Intelligence, 2006, 19(3): 289-294.)
[16] Xie Y, Raghavan V V, Dhatric P, et al. A New Fuzzy Clustering Algorithm for Optimally Finding Granular Prototypes[J]. International Journal of Approximate Reasoning, 2005, 40(1-2): 109-124.
[17] 赵洁,董振宁,张沙清,等. 一种多粒度 Web 使用数据收集方法[J]. 现代图书情报技术, 2011(2): 42-47. (Zhao Jie, Dong Zhenning, Zhang Shaqing, et al. A Collection Method for Multi-granularity Web Usage Data[J].New Technology of Library and Information Service, 2011(2): 42-47.)
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