Using SOFM to improve web site text content

Sebastían A. Ríos, Juan D. Velásquez, Eduardo S. Vera, Hiroshi Yasuda, Terumasa Aoki

Research output: Contribution to journalConference articlepeer-review

12 Citations (Scopus)

Abstract

We introduce a new method to improve web site text content by identifying the most relevant free text in the web pages. In order to understand the variations in web page text, we collect pages during a period. The page text content is then transformed into a feature vector and is used as input of a clustering algorithm (SOFM), which groups the vectors by common text content. In each cluster, a centroid and its neighbor vectors are extracted. Then using a reverse clustering analysis, the pages represented by each vector are reviewed in order to find the similar. Furthermore, the proposed method was tested in a real web site, proving the effectiveness of this approach.

Original languageEnglish
Pages (from-to)622-626
Number of pages5
JournalLecture Notes in Computer Science
Volume3611
Issue numberPART II
Publication statusPublished - 2005 Oct 24
EventFirst International Conference on Natural Computation, ICNC 2005 - Changsha, China
Duration: 2005 Aug 272005 Aug 29

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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