Binary Particle Swarm Optimization based Biclustering of Web usage Data

TL;DR

This paper introduces a novel biclustering algorithm based on Binary Particle Swarm Optimization to uncover hidden web usage patterns, enhancing e-commerce applications like advertising and marketing.

Contribution

It combines swarm intelligence with biclustering to effectively identify global optimal biclusters in web usage data, a novel approach in this context.

Findings

Successfully applied to real web usage datasets

Improves detection of user-page relationship patterns

Enhances e-commerce marketing strategies

Abstract

Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms.