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Title

An Effective Process for Finding Frequent Sequential Traversal Patterns on Varying Weight Range

Author

Abhilasha Vyas and Priyanka Dhasal

Vol. 16  No. 1  pp. 130-134

Abstract

Many frequent sequential traversal pattern mining algorithms have been developed which mine the set of frequent subsequences traversal pattern satisfying a minimum support constraint in a session database. However, previous frequent sequential traversal pattern mining algorithms give equal weightage to sequential traversal patterns while the pages in sequential traversal patterns have different importance and have different weightage. Another main problem in most of the frequent sequential traversal pattern mining algorithms is that they produce a large number of sequential traversal patterns when a minimum support is lowered and they do not provide alternative ways to adjust the number of sequential traversal patterns other than increasing the minimum support. In this paper, we propose a frequent sequential traversal pattern mining algorithm with dynamic weight constraint. Our main approach is to support the weight constraints into the sequential traversal pattern while maintaining the downward closure property with weight range. A weight range is defined to maintain the downward closure property and pages are given different range and traversal sequences assign a minimum and maximum weight. Here we are accessing pattern utility graph for dynamically approach. In scanning a session database, a maximum and minimum weight in the session database is used to prune infrequent sequential traversal subsequence by doing downward closure property can be maintained. Our method produces a few but important sequential traversal patterns in session databases with a low minimum support, by adjusting a weight range of pages and sequence.

Keywords

Sequential traversal pattern mining, Weight constraint, Web usage mining, Data mining

URL

http://paper.ijcsns.org/07_book/201601/20160119.pdf