A graph-based web usage mining method considering client side data
ABSTRACT To improve Web site, we need to evaluate current usage of it. Web usage mining and statistical analysis are two ways to evaluate usage of Web site. The combination of Web usage mining and statistical analysis gives more accurate information about Web usage. Through Web usage mining methods, graph mining covers complex Web browsing behaviors such as parallel browsing. Through statistical analysis methods, analyzing page browsing time gives valuable information about Web site and its users. This paper presents a Web usage mining method which combines Web usage mining and statistical analysis considering client side data. In other words, it combines graph based Web usage mining and browsing time analysis with taking client side data into account. It helps us to reconstruct user session exactly as it has been and based on these data, we find Web usage patterns with more accuracy.
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ABSTRACT: Summary A lot of real world problems can be modeled as traversals on graph, and mining from such traversals has been found useful in several applications. However, previous works considered only traversals on unweighted graph. This paper generalizes this to the case where vertices of graph are given weights to reflect their importance. Under such weight settings, traditional mining algorithms can not be adopted directly any more. To cope with the problem, this paper proposes new algorithms to discover weighted frequent patterns from the traversals. Specifically, we devise support bound paradigms for candidate generation and pruning during the mining process.
Conference Proceeding: Frequent subgraph discovery[show abstract] [hide abstract]
ABSTRACT: As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets is to use graphs. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs.The authors present a computationally efficient algorithm for finding all frequent subgraphs in large graph databases. We evaluated the performance of the algorithm by experiments with synthetic datasets as well as a chemical compound dataset. The empirical results show that our algorithm scales linearly with the number of input transactions and it is able to discover frequent subgraphs from a set of graph transactions reasonably fast, even though we have to deal with computationally hard problems such as canonical labeling of graphs and subgraph isomorphism which are not necessary for traditional frequent itemset discoveryData Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on; 02/2001
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ABSTRACT: The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.html KICSS 2007 : The Second International Conference on Knowledge, Information and Creativity Support Systems : PROCEEDINGS OF THE CONFERENCE, November 5-7, 2007, [Ishikawa High-Tech Conference Center, Nomi, Ishikawa, JAPAN] With the increase of large web sites which have complex link structures, web access logs have caught attention as a clue for web site administrators to understand user's needs and demands. While conventional statistical analysis is used for most of the cases, web usage mining is an emerging attempt to apply data-mining based technique to web access log analyses. However, statistical and data-mining based analyses have been independently applied, and no method has been reported to correlate their results yet. This paper introduces a novel web usage mining method to combine the statistical analysis of page browsing time and the graph based data mining technique in order to extract users' typical browsing behaviors.