Clustering Web Documents Using Ontology-Based Fuzzy Method

Web documents and web pages are expanding rapidly. Web search engines and web services use different methods to find web pages and documents in the massive amount of documents. However, organizing and analyzing a large amount of data is challenging. The problem with web page retrieval is that the information on the global web is in different formats and from different sources. The accuracy of data selection is essential and their compliance with user requests is a challenge in exploring the web. In order to provide an optimal solution for exploring web documents and organizing and providing quick and accurate access to structured and semi-structured Web documents and web pages, a new approach is proposed. The proposed method is based on the clustering and Web document fuzzation and the semantic and structure of web pages. In the proposed method for the reduction of dimension or features, the mapping of attributes to semantic domains is proposed. The results of the implementation of the proposed method in Python and MATLAB software show that the proposed method in categorizing and organizing web documents is appropriate for the quality of clusters and their density, and in the terms of the davies bouldin and silhouette index, they have suitable values.

Keywords:

Clustering , Web Documents , Mining , Semantic Web