Paper Details: Downloads: 490
Serial Number: P1120535136
Title: WEB MINING BASED ON GENETIC ALGORITHM
Authors: M. H. Marghny and A. F. Ali
Abstract: As the web continues to increase in size, the relative coverage of web search engine is decreasing, and search tools that combine the results of multiple search engines are becoming more valuable. We propose a framework for web mining, the applications of data mining and knowledge discovery techniques to data collected in World Wide Web (WWW), and a genetic search for search engines by showing that important relation existed between web statistical studies and search engines standard techniques in optimization. It is straightforward to define an evaluation function that is a mathematical formulation of the user request and to define a steady state genetic algorithm (GA) that evolves a population of pages with binary tournament selection. Querying standard search engine performs the creation of individuals. The crossover operator that with probability of crossover Pc is performed by selecting two parent individuals (web pages) from the population. It chooses one crossover position within the page randomly and exchanges the links after that position between both individuals (web pages). We present a comparative evaluation that is performed with the same protocol as used in optimization. Our tool leads to pages of qualities that are significantly better than those of the standard search engines.
Keywords: search engines, Meta search, crossover, genetic algorithm, web mining.
Journal/Conference: ICGST Conference on Artificial Intelligence and Machine Learning, AIML-05
Volume:
Issue:
Submission Date: 8/1/2005 12:00:00 AM
Review Date: 10/1/2005 12:00:00 AM
Publishing Date: 12/19/2005 12:00:00 AM
Article Downloads: 490
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