Using SOFM to improve web site text content

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

研究成果: Conference article査読

12 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)622-626
ページ数5
ジャーナルLecture Notes in Computer Science
3611
PART II
出版ステータスPublished - 2005 10 24
イベントFirst International Conference on Natural Computation, ICNC 2005 - Changsha, China
継続期間: 2005 8 272005 8 29

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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