A Survey on Preprocessing Methods for Web Usage Data

TL;DR

This survey reviews preprocessing techniques for web usage data, emphasizing their importance in web mining, and discusses various data mining methods and applications in analyzing web logs.

Contribution

It provides a comprehensive overview of preprocessing methods in web usage mining, highlighting their role and challenges in extracting user behavior patterns.

Findings

Preprocessing is crucial for effective web usage mining.

Various data mining techniques are applied in pattern discovery.

Web usage mining has diverse applications like personalization and profiling.

Abstract

World Wide Web is a huge repository of web pages and links. It provides abundance of information for the Internet users. The growth of web is tremendous as approximately one million pages are added daily. Users' accesses are recorded in web logs. Because of the tremendous usage of web, the web log files are growing at a faster rate and the size is becoming huge. Web data mining is the application of data mining techniques in web data. Web Usage Mining applies mining techniques in log data to extract the behavior of users which is used in various applications like personalized services, adaptive web sites, customer profiling, prefetching, creating attractive web sites etc., Web usage mining consists of three phases preprocessing, pattern discovery and pattern analysis. Web log data is usually noisy and ambiguous and preprocessing is an important process before mining. For discovering patterns sessions are to be constructed efficiently. This paper reviews existing work done in the preprocessing stage. A brief overview of various data mining techniques for discovering patterns, and pattern analysis are discussed. Finally a glimpse of various applications of web usage mining is also presented.