Accuracy of Textual Document Clustering With Semantic Approach
Natural Language Processing with Semantic by the help of WordNet. The accuracy of Clustering is assured by F-Measure
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Now the age of information technology, textual document is spontaneously increasing over the internet, e-mail, web pages, offline & online reports, journals, articles and those are stored in the electronic database format. Millions of new text file created in a day, for the lackings of classification, people miss vast information those are useful to several challenges. To maintain and access those documents are very difficult without adequate rating and when there has classification without any information provide call clustering. To overcome such difficulties K-means and others old clustering...
Now the age of information technology, textual document is spontaneously increasing over the internet, e-mail, web pages, offline & online reports, journals, articles and those are stored in the electronic database format. Millions of new text file created in a day, for the lackings of classification, people miss vast information those are useful to several challenges. To maintain and access those documents are very difficult without adequate rating and when there has classification without any information provide call clustering. To overcome such difficulties K-means and others old clustering algorithms are unfit to impart as may be expected on Natural languages. Because of high-dimensional about texts, the presence of logical structure clues within the texts and novel segmentation techniques have taken advantage of advances in generative topic modeling algorithms, specifically designed to spot questions at intervals text to cipher word topic distributions. So considering the limitation, COBWEB conceptual clustering algorithm applied to the preprocessed data. For ensuring the accuracy of clusters, the f-measure accuracy measuring methods selected for evaluating the clusters.
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