An Efficient Preprocessing Methodology for Discovering Patterns and Clustering of Web Users using a Dynamic ART1 Neural Network

TL;DR

This paper introduces a preprocessing approach for web usage mining that reduces data size and employs a dynamic ART1 neural network to effectively cluster web users based on their access patterns.

Contribution

It presents a novel preprocessing methodology combined with a dynamic ART1 neural network clustering algorithm for web usage analysis.

Findings

Data size reduced by 73-82% using the preprocessing method.

The ART1 clustering algorithm produces stable, quality clusters.

The methodology enhances web usage pattern discovery.

Abstract

In this paper, a complete preprocessing methodology for discovering patterns in web usage mining process to improve the quality of data by reducing the quantity of data has been proposed. A dynamic ART1 neural network clustering algorithm to group users according to their Web access patterns with its neat architecture is also proposed. Several experiments are conducted and the results show the proposed methodology reduces the size of Web log files down to 73-82% of the initial size and the proposed ART1 algorithm is dynamic and learns relatively stable quality clusters.