Volume 4 Number 5 (Jul. 2009)
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JSW 2009 Vol.4(5): 460-468 ISSN: 1796-217X
doi: 10.4304//jsw.4.5.460-468

Research on Web Session Clustering

Li Chaofeng
College of Management, South-Central University for Nationalities, Wuhan , P.R. China

Abstract—The task of clustering web sessions is to group web sessions based on similarity and consists of maximizing the intra-group similarity while minimizing the inter-group similarity. The results of Web session clustering can be used in personalization, system improvement, site modification, business intelligence, usage characterization and so forth. This paper proposes a framework of Web session clustering first. Then several data preparation techniques that can be used to improve the performance of data preprocessing are presented. A new method for measuring similarities between web pages that takes into account not only the URL but also the viewing time of the visited web page is also introduced and a new method to measure the similarity of web sessions using sequence alignment and the similarity of web page access is given in detail. Finally, an algorithm of web session clustering is proposed. This algorithm defines the number of clusters according to the knowledge of application fields, takes advantage of ROCK to decide the initial data points of each cluster and determines the criterion function according to the contributions of overall increase in similarities made by dividing Web sessions into different clusters --- which not only overcomes the shortcomings of traditional clustering algorithm which merely focus on partial similarities, but also decreases the complexities of time and space.

Index Terms—Web session clustering; Data Preprocessing; sequence alignment; similarity measurement


Cite: Li Chaofeng, "Research on Web Session Clustering," Journal of Software vol. 4, no. 5, pp. 460-468, 2009.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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