An efficient model for information gain of sequential pattern from web logs based on dynamic weight constraint

Abstract

Data mining is the task of discovering interesting patterns from large amounts of data. There are many data mining tasks, such as classification, clustering, association rule mining, and sequential pattern mining. 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 with weights constraint. Our main approach is to add the weight constraints into the sequential traversal pattern while maintaining the downward closure property. A weight range is defined to maintain the downward closure property and pages are given different weights and traversal sequences assign a minimum and maximum weight. 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.