Browse > Article
http://dx.doi.org/10.3745/KIPSTD.2008.15-D.1.139

A Study on Behavior Rule Induction Method of Web User Group using 2-tier Clustering  

Hwang, Jun-Won (건국대학교 컴퓨터공학과)
Song, Doo-Heon (용인송담대학 컴퓨터게임정보과)
Lee, Chang-Hoon (건국대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartD / v.15D, no.1, 2008 , pp. 139-146 More about this Journal
Abstract
It is very important to identify useful web user group and induce their behavior pattern in eCRM domain. Inducing user group with a similar inclination, a reliability of user group decreases because there is an uncertainty in online user data. In this paper, we have applied the 2-tier clustering, which uses the outcome of interaction with data from other tiers. Also we propose a method which induces user behavior pattern from a cluster and compare C4.5 with our method.
Keywords
Web Mining; Clustering; eCRM; C4.5;
Citations & Related Records
연도 인용수 순위
  • Reference
1 B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, 'Web Data Mining: Effective personalization based on association rule discovery from web usage data,' Proceedings of the 3rd international workshop on Web information and data management, pp.9-15, 2001
2 R. Kosala, and H. Blockeel, 'Web Mining Research: A Survey,' ACM SIGKDD Explorations, Vol.2 No.1, pp.1-15, 2000
3 Vincent S. Tseng, Jeng-Chuan Chang, and Kawuu W. Lin, 'Electronic commerce technologies(ECT): Mining and Prediction of Temporal Navigation patterns for personalized services in e-commerce,' Proceedings of the 2006 ACM symposium on Applied computing, pp.867-871, 2006
4 Y. Xie, and V. V. Phoha, 'Web user clustering from access log using belief function,' Proceedings of the 1st international conference on Knowledge capture, pp.202-208, 2001
5 M. Gery, and H. Haddad, 'Web clustering and usage mining: Evaluation of Web Usage Mining Approacvhes for User's Next Request Prediction,' Proceedings of the 5th ACM international workshop on Web information and data management, pp.74-81, 2003
6 J. W. Hwang, D. H. Song, and C. H. Lee, 'Performance Analysis of 2-tier Clustering,' 2006 International Conference Hybrid Information Technology, vol.2, pp.542-547, 2006
7 J. R. Quinlan, 'C4.5: Programs for Machine Learning,' Morgan Kaufmann Publishers, 1993
8 J. Srivastava, R. Cooley, M. Deshpande, and P. N. Tan, 'Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data,' ACM SIGKDD Explorations, Vol.1 No.2, pp.12-23, 2000   DOI
9 Brian S Everitt, 'Hierarchical Clustering Techniques', Cluster Analysis, pp.55-90, 1993
10 H.J. Zeng, Z. Checn, and W. Y. Ma, 'A Unified Framework for Clustering Heterogeneous Web Objects,' Proceedings of the 3rd International Conference on Web Information Systems Engineering, pp.161-172, 2002
11 C. Stanfill, and D. Waltz, 'Toward memory-based reasoning,' Communications of the ACM, Vol.29, No.12, 1986
12 M. Steinbach, G. Karypis, and V. Kumar, 'A Comparison of document clustering techniques,' KDD Workshop on Text Mining, 2000
13 Open Directory Project, http://dmoz.org/, 2007