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

S. K. Ahammad Fahad

ISBN 10: 6133991720 / ISBN 13: 9786133991729

Editore: LAP LAMBERT Academic Publishing, 2017

Riguardo questo articolo

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Codice articolo 9786133991729

Dati bibliografici

• Titolo: 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
• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2017
• Lingua: Inglese
• ISBN 10: 6133991720
• ISBN 13: 9786133991729
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 220 L x 150 A x 9 P (millimetri) 220 millimetri di larghezza per 150 millimetri di altezza per 9 millimetri di profondità
• Peso: 233 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Mr. SK Ahammad Fahad achieves his Master’s from Al-Madinah International University (Malaysia). He earned a Bachelor's degree at IBAIS University (Bangladesh). Currently, he is working in Natural Language Processing with semantics and lexicon database on different Languages.