Abstract
Mining frequent web access patterns from large data (web log) is one significant application of sequential pattern mining. Web access patterns are set of frequent sub sequences that are useful to know user behaviour in real time in order to make dynamic decisions. Techniques for extracting web access patterns from data available in two flavours: apriori based and non apriori based (tree based). It has been observed that extracting web access pattern with respect to multiple dimensions gives interesting results rather than considering one dimension. In this paper a very interesting data structure, multidimensional web access pattern tree (MD-WAP Tree) is presented that can discover web access pattern with respect to multiple dimensions known as multidimensional web access patterns.
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