Frequent Pattern Mining in Web Log Data
#1

Abstract:
Frequent pattern mining is a heavily researched area in the field of data mining
with wide range of applications. One of them is to use frequent pattern discovery methods
in Web log data. Discovering hidden information from Web log data is called Web usage
mining. The aim of discovering frequent patterns in Web log data is to obtain information
about the navigational behavior of the users. This can be used for advertising purposes, for
creating dynamic user profiles etc. In this paper three pattern mining approaches are
investigated from the Web usage mining point of view. The different patterns in Web log
mining are page sets, page sequences and page graphs.
Keywords: Pattern mining, Sequence mining, Graph Mining, Web log mining
1 Introduction
The expansion of the World Wide Web (Web for short) has resulted in a large
amount of data that is now in general freely available for user access. The
different types of data have to be managed and organized in such a way that they
can be accessed by different users efficiently. Therefore, the application of data
mining techniques on the Web is now the focus of an increasing number of
researchers. Several data mining methods are used to discover the hidden
information in the Web. However, Web mining does not only mean applying data
mining techniques to the data stored in the Web. The algorithms have to be
modified such that they better suit the demands of the Web. New approaches
should be used which better fit the properties of Web data. Furthermore, not only
data mining algorithms, but also artificial intelligence, information retrieval and
natural language processing techniques can be used efficiently. Thus, Web mining
has been developed into an autonomous research area.

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