Extracting Sequential Access Pattern from Pre-Processed Web Logs

Abstract

Abstract-Finding Frequent Sequential Pattern (FSP) is an important problem in web usage mining. In this paper, we systematically explore a pattern-growth approach for efficient mining of sequential patterns in large sequence database. The approaches adopts a (divide and conquer) pattern-growth principle as follows: Sequence databases are recursively projected into a set of smaller projected databases based on the current sequential pattern(s), and sequential patterns are grown in each projected databases by exploring only locally frequent fragments. Our proposed method combines tree projection and prefix growth features from pattern-growth category with position coded feature from early-pruning category, all of these features are key characteristics of their respective categories, so we consider our proposed method as a pattern growth / early-pruning hybrid algorithm that considerably reduces execution time. These approaches were implemented in hybrid concrete method using algorithms of sequential pattern mining.