Improved Preprocessing Algorithms to Filter Web Log Data based on Specific Requirements of Pattern Analysis

Abstract

Preprocessing is the most important and time consuming step in web log mining. Results of web log mining mostly depend on extensive and correct preprocessing. Due to the drastic change in data consumption by users, there is a need of time to improve classical preprocessing algorithms. Although many algorithms exist for preprocessing, there is a need for an algorithm which can dynamically change the results of preprocessing according to need. Also there is a drastic need to add a few more parameters in earlier preprocessing algorithms to reduce the error rate. In this paper, we have introduced preprocessing algorithms for web usage data by inculcating some new parameters. These parameters make results more accurate for effective generation of web usage patterns.