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 language | English |
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Pages (from-to) | 622-626 |
Number of pages | 5 |
Journal | Lecture Notes in Computer Science |
Volume | 3611 |
Issue number | PART II |
Publication status | Published - 2005 Oct 24 |
Event | First International Conference on Natural Computation, ICNC 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)