Preprocessing: A Prerequisite for Discovering Patterns in WUM Process

TL;DR

This paper presents a comprehensive preprocessing methodology for web log data, significantly reducing data size and enhancing structure to facilitate more effective web usage pattern discovery.

Contribution

It introduces a complete preprocessing framework including merging, cleaning, session identification, and formatting, validated by experiments showing substantial data size reduction and improved data quality.

Findings

Reduces web log data size to 73-82% of original

Produces richer, structured logs for analysis

Enhances pattern discovery in web usage mining

Abstract

Web log data is usually diverse and voluminous. This data must be assembled into a consistent, integrated and comprehensive view, in order to be used for pattern discovery. Without properly cleaning, transforming and structuring the data prior to the analysis, one cannot expect to find meaningful patterns. As in most data mining applications, data preprocessing involves removing and filtering redundant and irrelevant data, removing noise, transforming and resolving any inconsistencies. In this paper, a complete preprocessing methodology having merging, data cleaning, user/session identification and data formatting and summarization activities to improve the quality of data by reducing the quantity of data has been proposed. To validate the efficiency of the proposed preprocessing methodology, several experiments are conducted and the results show that the proposed methodology reduces the size of Web access log files down to 73-82% of the initial size and offers richer logs that are structured for further stages of Web Usage Mining (WUM). So preprocessing of raw data in this WUM process is the central theme of this paper.