A Hybrid Approach for ImprovingWeb Document Clustering Based on Concept Mining

Abstract

With the rapid development of internet, huge volumes of text data are also increasing. This rapidly growing data generate challenges for users like access, organize and analyze the required information expeditiously. Document clustering techniques mostly rely on the statistical analysis of a term. It can hard to identify, in situation when multiple terms have the same frequency value, but one term is more important in terms of meaning than the other. Also, process to discover more relevant information regarding user query on the web is uncontrollable. The proposed system tries to implement a concept based document clustering model that clusters the web documents based on the semantics or theme of the text data. The semantic analysis is done with the help of Semantic Role Labeler (SRL), to find the terms which contribute more to the meaning of the sentence. This system is called as a Concept Based Analysis Mechanism (CBAM). This underlying model provides robust and accurate document similarity calculation that leads to improved results in Web document clustering over traditional methods