Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (30): 12-14.

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Web users clustering analysis based on AFSA

ZANG Wenke1,LIU Xiyu2   

  1. 1.School of Mathematical Science,Shandong Normal University,Jinan 250014,China
    2.School of Management Science and Engineering,Shandong Normal University,Jinan 250014,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2011-10-21 Published:2011-10-21



  1. 1.山东师范大学 数学科学学院,济南 250014
    2.山东师范大学 管理科学与工程学院,济南 250014

Abstract: The scalability of traditional clustering algorithm is not strong.Its capacity of processing isolated points is also weak.Artificial Fish Swarm Algorithm(AFSA) is an algorithm for global optimization based on animal behavior.It is used in web users clustering;imitating fish feeding,clustering,pileup and random acts to construct artificial fish.Through the local optimization of each individual fish,the global optimal value is found,and thus get reasonable clustering for web access users.The actual results verify that the algorithm is effective.

Key words: Artificial Fish Swarm Algorithm(AFSA), user clustering, log mining

摘要: 传统的可伸缩性聚类算法可扩展性不强、处理孤立点的能力较弱。人工鱼群算法是一种基于动物行为的寻求全局最优算法,将人工鱼群算法应用于Web用户聚类,模仿鱼群的觅食、聚群、追尾和随机行为来构造人工鱼,通过鱼群每个个体的局部最优,来找到全局最优值,从而对Web访问用户进行合理聚类。实际运行结果验证了算法的有效性。

关键词: 人工鱼群, 用户聚类, 日志挖掘