A Consistent Web Documents Based Text Clustering Using Concept Based Mining Model
Text mining is a growing innovative field that endeavors to collect significant information from natural language processing term. It might be insecurely distinguished as the course of examining texts to extract information that is practical for particular purposes. In this case, the mining model can detain provisions that identify the concepts of the sentence or document, which tends to detect the subject of the document. In an existing work, the concept-based mining model is used only for normal text documents clustering and clustered the text parts of the documents and efficiently discover noteworthy identical concepts among documents, according to the semantics of the sentences. But the downside of the work is that the existing work cannot be linked to web documents clustering and the text classification for the documents is an unreliable one. To make the text clustering more consistent, in our work, we plan to present a Conceptual Rule Mining On Text clusters to evaluate the more related and influential sentences contributing to the document topic. In this paper, the conceptual text clustering extends to web documents, containing various markup language formats associated with the documents (term extraction mode). Based on the markup languages like presentations, procedural and descriptive markup, the web document\'s text clustering is done efficiently using the concept-based mining model. Experiments are conducted with the web documents extracted from the research repositories to evaluate the efficiency of the proposed consistent web document\'s text clustering using conceptual rule mining with an existing An Efficient Concept-Based Mining Model for Enhancing Text Clustering.
Keywords: Concept-based mining model, sentence-based, web document-based, concept-based similarity, text clustering.
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ABOUT THE AUTHORS
V.M.Navaneethakumar
Mr. V.M. Navaneethakumar obtained M.C.A, from K.S.Rangasamy College of Technology, Tiruchengode Tamil Nadu, India, in 2004, and M.Phil., Computer Science from Periyar University, Salem, Tamilnadu, India in 2008. He is working as Assistant Professor, in Department of Computer Applications, K.S.R College of Engineering, Tiruchengode, Tamilnadu, India
C.Chandrasekar
Dr. C. Chandrasekar completed his Ph.D in Periyar University, Salem at 2006. He worked as Head, Department of Computer Applications at K.S.R. College of Engineering, Tiruchengode, Tamil Nadu, India. Currently he is working as Associate Professor in the Department of Computer Science at Periyar University, Salem, Tamilnadu. His research interest includes Mobile computing, Networks, Image processing and Data mining. He is a senior member of ISTE and CSI.
V.M.Navaneethakumar
Mr. V.M. Navaneethakumar obtained M.C.A, from K.S.Rangasamy College of Technology, Tiruchengode Tamil Nadu, India, in 2004, and M.Phil., Computer Science from Periyar University, Salem, Tamilnadu, India in 2008. He is working as Assistant Professor, in Department of Computer Applications, K.S.R College of Engineering, Tiruchengode, Tamilnadu, India
C.Chandrasekar
Dr. C. Chandrasekar completed his Ph.D in Periyar University, Salem at 2006. He worked as Head, Department of Computer Applications at K.S.R. College of Engineering, Tiruchengode, Tamil Nadu, India. Currently he is working as Associate Professor in the Department of Computer Science at Periyar University, Salem, Tamilnadu. His research interest includes Mobile computing, Networks, Image processing and Data mining. He is a senior member of ISTE and CSI.
