CAS based clustering algorithm for Web users

• Published in: Nonlinear dynamics Vol. 61; no. 3; pp. 347 - 361
• Main Authors: Wan, Miao, Li, Lixiang, Xiao, Jinghua, Yang, Yixian, Wang, Cong, Guo, Xiaolei
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2010; Springer Nature B.V
• Subjects: Algorithms; Automotive Engineering; Classical Mechanics; Clustering; Control; Dynamical Systems; Engineering; Market segmentation; Mechanical Engineering; Optimization; Statistical analysis; Vibration; Clustering; Chaotic ant swarm (CAS); Web access logs; Web user clustering
• ISSN: 0924-090X; 1573-269X
• DOI: 10.1007/s11071-010-9653-2

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.