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Title: Meta data conceptual mining model using analysis of bilateral intelligence for effective text clustering
Researcher: Koteeswaran, S
Guide(s): Kannan, E
Keywords: Computer Science
Data Mining
Text Mining
Levenberg Marquardt algorithm
Upload Date: 20-Feb-2014
University: Vel Tech Dr. R R and Dr. S R Technical University
Completed Date: March 2013
Abstract: Data mining from organizational data for extracting hidden knowledge is a growing field of study, which forms new study groups named knowledge discovery in database. Engineering applications of data mining which include, web mining, network mining, image mining and multimedia mining in general and text mining in particular. Predicting business flow, finding recent trends, sales and markets monitoring, forecasts, competition monitoring, expenditures control, revenues and administrative processes, finding volcanoes on Venus, Earth geophysics, earthquake photography from space, atmospheric science are some of the real world applications of data mining. Around 80% of data in the world are stored in unstructured textual format. Hence, the text mining and document clustering are major research areas in the past few years. Designing a effective and novel text mining requires high dimensionality, dynamic methodology, fast information access, specialized knowledge extraction from very huge data sets. Variety of approaches are proposed in the literature and these approaches vary with a range of tranditional k-means algorithm, term based methods, pattern taxonomy model and rank based methods. Existing text mining methods has many pitfalls like slow processing, lesser scalability, unable to solve conceptual problems like synonomy and polysemy. Therefore, this thesis focused text mining, proposed conceptual analysis and concentrated to solve conceptual problem like synonomy. Initially, this research work proposed metadata conceptual mining model for effective text mining. The proposed work executes in two phases of manipulation, which are training phase and testing phase. Initially in the pre-processing stage, the di-grams such as in, as, it; and tri-grams such as are, for, ing are removed from the documents. The proposed work created a data structure called, Significant Term List (STL) for each category of documents. A list of keywords on each domain of study and for each field of study is added in the concern STL.
Pagination: xvii, 147p.
Appears in Departments:Department of Computer Science and Engineering

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01_title.pdfAttached File66.63 kBAdobe PDFView/Open
02_certificate.pdf264.33 kBAdobe PDFView/Open
03_declaration.pdf268.8 kBAdobe PDFView/Open
04_abstract.pdf26.59 kBAdobe PDFView/Open
05_acknowledgements.pdf35.49 kBAdobe PDFView/Open
06_contents.pdf26.08 kBAdobe PDFView/Open
07_list of tables.pdf18.25 kBAdobe PDFView/Open
08_list of figures.pdf22.83 kBAdobe PDFView/Open
09_list of abbreviations.pdf21.71 kBAdobe PDFView/Open
10_chapter 1.pdf143.1 kBAdobe PDFView/Open
11_chapter 2.pdf139.84 kBAdobe PDFView/Open
12_chapter 3.pdf194.79 kBAdobe PDFView/Open
13_chapter 4.pdf420.77 kBAdobe PDFView/Open
14_chapter 5.pdf133.4 kBAdobe PDFView/Open
15_chapter 6.pdf15.51 kBAdobe PDFView/Open
16_references.pdf67.55 kBAdobe PDFView/Open
17_publications.pdf14.41 kBAdobe PDFView/Open
18_biography.pdf11.68 kBAdobe PDFView/Open

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