A reasonable rough approximation for clustering web users

Authors:
Duoqian Miao;Min Chen;Zhihua Wei;Qiguo Duan
Affiliations:
Department of Computer Science and Technology, Tongji University, Shanghai, China and The Key Laboratory of Embedded System and Service Computing, Ministry of Education, China;Department of Computer Science and Technology, Tongji University, Shanghai, China and The Key Laboratory of Embedded System and Service Computing, Ministry of Education, China;Department of Computer Science and Technology, Tongji University, Shanghai, China and The Key Laboratory of Embedded System and Service Computing, Ministry of Education, China;Department of Computer Science and Technology, Tongji University, Shanghai, China and The Key Laboratory of Embedded System and Service Computing, Ministry of Education, China
Venue:
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Year:
2006

Citing 6
Cited 3

Mining e-commerce data: the good, the bad, and the ugly

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Web Mining: Information and Pattern Discovery on the World Wide Web

ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Interval Set Clustering of Web Users with Rough K-Means

Journal of Intelligent Information Systems
Rough set based incremental clustering of interval data

Pattern Recognition Letters
Some refinements of rough k-means clustering

Pattern Recognition
Some new indexes of cluster validity

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Outlier Detection Based on Granular Computing

RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
An improved rough clustering using discernibility based initial seed computation

ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Semantic Web search based on rough sets and Fuzzy Formal Concept Analysis

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Due to the uncertainty in accessing Web pages, analysis of Web logs faces some challenges. Several rough k-means cluster algorithms have been proposed and successfully applied to Web usage mining. However, they did not explain why rough approximations of these cluster algorithms were introduced. This paper analyzes the characteristics of the data in the boundary areas of clusters, and then a rough k-means cluster algorithm based on a reasonable rough approximation (RKMrra) is proposed. Finally RKMrra is applied to Web access logs. In the experiments RKMrra compares to Lingras and West algorithm and Peters algorithm with respect to five characteristics. The results show that RKMrra discovers meaningful clusters of Web users and its rough approximation is more reasonable.