"Improved FCM algorithm for Clustering on Web Usage Mining"

TL;DR

This paper introduces an improved fuzzy c-means clustering algorithm that uses information entropy for better initialization and weighting to handle noise, enhancing web usage data clustering accuracy.

Contribution

The proposed method enhances FCM clustering by incorporating entropy-based initialization and weighting, specifically addressing sensitivity to initial centers and noise in web data.

Findings

Improved clustering accuracy on MSNBC web navigation data

Better handling of noisy web log data

Enhanced initialization reduces convergence issues

Abstract

In this paper we present clustering method is very sensitive to the initial center values, requirements on the data set too high, and cannot handle noisy data the proposal method is using information entropy to initialize the cluster centers and introduce weighting parameters to adjust the location of cluster centers and noise problems.The navigation datasets which are sequential in nature, Clustering web data is finding the groups which share common interests and behavior by analyzing the data collected in the web servers, this improves clustering on web data efficiently using improved fuzzy c-means(FCM) clustering. Web usage mining is the application of data mining techniques to web log data repositories. It is used in finding the user access patterns from web access log. Web data Clusters are formed using on MSNBC web navigation dataset.