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Clustering Techniques in Web Content Mining

Author(s): Ranjit R.Keole | G.R.Bamnote

Journal: International Journal of Advanced Research in Computer Science
ISSN 0976-5697

Volume: 01;
Issue: 04;
Start page: 225;
Date: 2010;
Original page

Keywords: k-means | cure | birch | rock | erock | fuzzy | clustering | text | mining.

Clustering is useful technique in the field of textual data mining. Cluster analysis divides objects into meaningful groups based on similarity between objects. Copious material is available from the World Wide Web (WWW) in response to any user-provided query. It becomes tedious for the user to manually extract real required information from this material. Large document collections, such as those delivered by Internet search engines, are difficult and time-consuming for users to read and analyze. The detection of common and distinctive topics within a document set, together with the generation of multi-document summaries, can greatly ease the burden of information management. This paper focus on this problem of mining the useful information from the collected web documents using fuzzy clustering of the text collected from the downloaded web documents.
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