Journal article
Analysis of Browsing Behaviors with Ant Colony Clustering Algorithm
Journal of computers, v 7(12), pp 3096-3102
01 Dec 2012
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.
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Details
- Title
- Analysis of Browsing Behaviors with Ant Colony Clustering Algorithm
- Creators
- Xiaohua Hu - Second Mil Med Univ, Changhai Hosp, Shanghai 200438, Peoples R ChinaTao Mu - Fudan Univ, Informat Ctr, Shanghai 200433, Peoples R ChinaWeihui Dai - Fudan Univ, Informat Ctr, Shanghai 200433, Peoples R ChinaHongzhi Hu - Fudan Univ, Informat Ctr, Shanghai 200433, Peoples R ChinaGenghui Dai - Fudan Univ, Informat Ctr, Shanghai 200433, Peoples R China
- Publication Details
- Journal of computers, v 7(12), pp 3096-3102
- Publisher
- Acad Publ
- Number of pages
- 7
- Grant note
- 90924013 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000218143900032
- Scopus ID
- 2-s2.0-84871483277
- Other Identifier
- 991019189026004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications