Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming

TL;DR

This paper introduces a novel approach combining ant colony clustering and genetic programming to analyze web usage data, aiming to improve pattern discovery and visitor trend analysis for better web management.

Contribution

It presents an innovative ant clustering algorithm for web usage pattern discovery and applies linear genetic programming for trend analysis, enhancing existing data mining techniques.

Findings

Ant colony clustering outperforms self-organizing maps in web pattern clustering.

Genetic programming effectively analyzes visitor trends.

Performance is slightly less accurate than evolutionary-fuzzy clustering.

Abstract

The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others. In this paper, we propose an ant clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly shows that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that efficient when comparared to evolutionary-fuzzy clustering (i-miner) approach. KEYWORDS: Web Usage Mining, Swarm Intelligence, Ant Systems, Stigmergy, Data-Mining, Linear Genetic Programming.