(untitled) (International Journal of Engineering and Computer Science, 2017)
With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. A considerably large portion of information present on the world wide web (www) today is in the form of unstructured or semi-structured text data bases. 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. Clustering is useful technique in the field of textual data mining. Cluster analysis divides objects into meaningful groups based on similarity between objects. This paper focus on this problem of mining the useful information from the collected web documents using clustering techniques of the text collected from the downloaded web documents.