Title :
A Genetic System for Cluster Analysis for Hypertext Documents
Author :
di Carlantonio, L.M. ; da Costa, R.M.E.
Author_Institution :
Univ. do Estado do Rio de Janeiro - UERJ, Rio de Janeiro
Abstract :
Due to the increase in the number of WWW home pages, we are facing new challenges for information retrieval and indexing. Some "intelligent" techniques, including neural networks, symbolic learning, and genetic algorithms have been used to group different classes of data in an efficient way. This article describes a system for cluster analysis of hypertext documents based on genetic algorithms. The effectiveness of the system in getting groups with similar documents is evidenced by the experimental results.
Keywords :
Internet; Web sites; genetic algorithms; hypermedia; information retrieval; neural nets; pattern clustering; statistical analysis; WWW home pages; cluster analysis; genetic algorithms; genetic system; hypertext documents; information retrieval; neural networks; symbolic learning; Algorithm design and analysis; Clustering algorithms; Data mining; Genetic algorithms; HTML; Information analysis; Intelligent systems; System analysis and design; Text analysis; Vectors;
Conference_Titel :
Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on
Conference_Location :
Rio de Janeiro
Print_ISBN :
978-0-7695-2976-9
DOI :
10.1109/ISDA.2007.126
