Web Log Data Analysis by Enhanced Fuzzy C Means Clustering

TL;DR

This paper introduces an enhanced fuzzy C-means clustering algorithm tailored for web log data analysis, aiming to improve user session clustering accuracy in web usage mining applications.

Contribution

The paper proposes a novel fuzzy C-means clustering method specifically designed for web log data, demonstrating improved accuracy over existing techniques.

Findings

Enhanced clustering accuracy demonstrated

Better performance in web log mining tasks

Effective grouping of user sessions

Abstract

World Wide Web is a huge repository of information and there is a tremendous increase in the volume of information daily. The number of users are also increasing day by day. To reduce users browsing time lot of research is taken place. Web Usage Mining is a type of web mining in which mining techniques are applied in log data to extract the behaviour of users. Clustering plays an important role in a broad range of applications like Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others. Clustering is the grouping of similar instances or objects. The key factor for clustering is some sort of measure that can determine whether two objects are similar or dissimilar . In this paper a novel clustering method to partition user sessions into accurate clusters is discussed. The accuracy and various performance measures of the proposed algorithm shows that the proposed method is a better method for web log mining.