Abstract
Discovery of information in web log data is a very popular research area in the field of data mining. Two of the objectives of favorite applications are to obtain useful information of web users’ behavior and to analyze the structure of web sites. In this paper, we suggest a novel approach to generate web sequential patterns using the gap-constrained method in web log data. The process of mining task in the proposed approach is described as follows. First, pre-process of the raw web log data is introduced by removing irrelevant or redundant items, gathering the same users and transforming the web log data into a set of tuples (sequence identifier, sequence) constrained by visiting time. Second, web access patterns, which are closed sequential patterns with gap constraints, are generated using the Gap-BIDE algorithm in web log data with two parameters, minimum support threshold and gap constraint. In the experiment, a data set is derived from http://www.vtsns.edu.rs/maja/, which is proposed in [1]. The result shows that, with the application of sequential pattern mining in the web log data presented in this paper, we can find information about navigational behavior of web users and the structure of the web page can be designed more legitimately by the order of obtained patterns.
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Yu, X., Li, M., Lee, D.G., Kim, K.D., Ryu, K.H. (2012). Application of Closed Gap-Constrained Sequential Pattern Mining in Web Log Data. In: Zeng, D. (eds) Advances in Control and Communication. Lecture Notes in Electrical Engineering, vol 137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-26007-0_80
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DOI: https://doi.org/10.1007/978-3-642-26007-0_80
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