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CAS based clustering algorithm for Web users
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DOI：10.1007/s11071-010-9653-2.png)
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Abstract
En 中文
This article devises a clustering technique for detecting groups of Web users from Web access logs. In this technique, Web users are clustered by a new clustering algorithm which uses the mechanism analysis of chaotic ant swarm (CAS). This CAS based clustering algorithm is called as CAS-C and it solves clustering problems from the perspective of chaotic optimization. The performance of CAS-C for detecting Web user clusters is compared with the popular clustering method named k-means algorithm. Clustering qualities are evaluated via calculating the average intra-cluster and inter-cluster distance. Experimental results demonstrate that CAS-C is an effective clustering technique with larger average intra-cluster distance and smaller average inter-cluster distance than k-means algorithm. The statistical analysis of resulted distances also proves that the CAS-C based Web user clustering algorithm has better stability. In order to show the utility, the proposed approach is applied to a pre-fetching task which predicts user requests with encouraging results.
Keywords:
Clustering
Chaotic ant swarm (CAS)
Web access logs
Web user clustering
Journal
IF:
6
Papers:
1.4W
Citations:
4.6W
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Cited Papers
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