WEB DOCUMENT CLUSTERING USING FUZZY CLUSTERING : AN APPROACH FOR IMPROVING DOCUMENT INSPECTION

Document Clustering is becoming vital for obtaining good results using unsupervised learning methods The approaches such as extractions and clustering are being increasingly used to improve the Document Clustering techniques. These approaches help reduce the problems in designing a general purpose document clustering. The traditional fuzzy clustering methods are not suitable for sentence clustering because it is difficult to depict most of the sentence similarity measures in a common metric space. An enhanced Fuzzy clustering algorithm can be applied to the sentences of data sets to group the related sentences and documents. This paper discusses two major sequential stages in Web Document Clustering “Extraction Features and Fuzzy Clustering Algorithm” as well as the major challenges and the key issues in designing extraction features and clustering algorithms. These methods aid performance enhancement and help speed up the solving of crimes by the law enforcement officers and detectives. In addition to web text domains, these algorithms can be incorporated for applications such as forensic analysis, data mining, bio-informatics, content-based or collaborative information filtering, social media, trend analysis, market analysis, banking sector and so forth.