Abstract

Sequential pattern mining discovers frequent user access patterns from web logs. Apriori-like sequential pattern mining techniques requires expensive multiple scans of database.  So, now days, WAP (Web Access Pattern) tree based algorithm is used. It is faster than traditional techniques.  However, the use of conditional search strategies in WAP-tree based mining algorithms requires re-construction of large numbers of intermediate conditional WAP-trees, which is also very costly.
In this paper, Kongu Arts and Science College (KASC) web logs are taken for mining. Here, we propose an efficient sequential pattern mining techniques for KASC web log access sequences known as CS-WAP Tree.  This proposed algorithm modifies the WAP tree approach for improving efficiency. The proposed algorithm totally eliminates the need to engage in numerous reconstructions of intermediate WAP trees and considerable reduces execution time. The results of experiments show the efficiency of the improved algorithm. The next key aim is to compare WAP algorithms.

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