A Hybrid Approach for Improving Web Document Clustering Based on Concept Mining

Abstract

With the rapid development of the Internet, huge volumes of text data are also increasing. These rapidly growing data generate challenges for users such as access, organizing, and analyzing the required information expeditiously. Document clustering techniques mostly rely on the statistical analysis of a term. It can be hard to identify in situations when multiple terms have the same frequency value, but one term is more important in terms of meaning than the other. Also, the process to discover more relevant information regarding a 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 the semantic role labeler (SRL) to find the terms that contribute more to the meaning of the sentence. This system is called a concept-based analysis mechanism (CBAM). This system can provide accurate and robust document similarity calculation as well as concept identification that lead to improved results in Web document clustering.