Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (22): 136-138.

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Hybrid Ant Colony Algorithm for Web user session clustering

LING Haifeng1,2, CAO Rongtao1,2   

  1. 1.School of Management, Hefei University of Technology, Hefei 230009, China
    2.Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China
  • Online:2013-11-15 Published:2013-11-15

基于混合蚁群算法的Web用户会话聚类

凌海峰1,2,曹荣涛1,2   

  1. 1.合肥工业大学 管理学院,合肥 230009
    2.过程优化与智能决策教育部重点实验室,合肥 230009

Abstract: Session clustering is an important technology of Web usage mining, aiming to find similar user behavior, which is one of the hot fields in electronic business. The difficulty of the problem lies in the large scale session data, which needs to be represented as the high dimensional vector, and which is a challenge to the performance of the algorithm. This paper presents a type of clustering algorithm combining ACO with PSO algorithm. Experimental results show that the algorithm has better performance compared with ACO, PSO and K-Means algorithm.

Key words: Web usage mining, Ant Colony Optimization(ACO), Partical Swarm Optimization(PSO), session clustering, e-business

摘要: 会话聚类是一种重要的Web使用挖掘技术,旨在发现相似的用户行为,这是目前电子商务中的热点问题之一。该问题的难度在于要对大规模的会话进行聚类,这些会话被表示成高维向量,加大了对算法高效性的要求。提出了一种ACO和PSO相结合的算法进行会话聚类分析。实验结果表明该算法与ACO算法、PSO算法、K-means算法相比,具有更好的性能。

关键词: Web使用挖掘, 蚁群优化, 粒子群优化, 会话聚类, 电子商务