Implementation Of Web Document Clustering Methods For Forensic Analysis

Web documents are diversified and complicated. Web documents involve complex associations and the linking to the other documents is also complicated. The interactions of terms in documents demonstrate imprecise and obscure meanings. There is need of systematic and effectual clustering methods to uncover untapped and rational meanings in context. Fuzzy clustering algorithm can be used to find the contextual meaning in the web documents. The main theme of clustering based techniques is to extract features from the web documents using conditional random field methods and build a fuzzy linguistic topological space based on the associations of features [1]. The associations of co-occurring features organize a hierarchy of connected semantic complexes called ‘CONCEPTS,’ wherein a fuzzy linguistic measure is applied on each complex to evaluate the relevance of a document belonging to a topic, and the difference between the other topics [1]. Web contents are able to be clustered into topics in the hierarchy depending on their fuzzy linguistic measures; web users can further explore the CONCEPTS of web contents accordingly [1]. Aside from web text domains, the algorithm can be applied to other applications such as forensic analysis, data mining, bioinformatics, social media, market analysis, banking sector and so forth.