Volume 7 Number 12 (Dec. 2012)
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JCP 2012 Vol.7(12): 3096-3102 ISSN: 1796-203X
doi: 10.4304/jcp.7.12.3096-3102

Analysis of Browsing Behaviors with Ant Colony Clustering Algorithm

Xiaohua Hu1, Tao Mu2, Weihui Dai3, Hongzhi Hu3, Genghui Dai4
1Changhai Hospital, Second Military Medical University, Shanghai 200438, P.R.China
2Information Centre, Fudan University, Shanghai 200433, China
3School of Management, Fudan University, Shanghai 200433, China
4School of Marine Sciences, Sun Yat-Sen University, Guangzhou 510275, China


Abstract—The characteristics of users' browsing behaviors on websites can be used to analyze system performance as well as network communication, understand users’ reaction and motivation, and build adaptive websites. However, the motivation, requirement and experience of users may dynamically change, which cause difficulty in exactly refining a stable behavior pattern and describing their shifted interest. This paper introduces an optimized ant colony clustering algorithm (OACA) in dynamic pattern discovery, and explores the structured formula to describe the users’ browsing behavior patterns as well as to analyze their characteristics adaptively. The test and results show that users are clustered accurately based on their similar browsing behavior from dynamic Web log data.

Index Terms—Internet User, Browsing Behavior, Swarm Intelligence, Clustering Analysis, Ant Colony Cluster Algorithm.

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Cite: Xiaohua Hu, Tao Mu, Weihui Dai, Hongzhi Hu, Genghui Dai, "Analysis of Browsing Behaviors with Ant Colony Clustering Algorithm," Journal of Computers vol. 7, no. 12, pp. 3096-3102, 2012.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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