Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/209167
Title: Semantics based Distributed Document Clustering
Researcher: Shah Ketan Neepa
Guide(s): Mahajan Sunita
Keywords: clustering methods
data mining techniques
Document clustering
intra-cluster
semantics
visualization
University: Narsee Monjee Institute of Management Studies
Completed Date: 2017
Abstract: Document clustering, one of the traditional data mining techniques, is an unsupervised learning paradigm where clustering methods try to identify inherent groupings of the text newlinedocuments. It produces a set of clusters that exhibit high intra-cluster similarity and low intercluster similarity. Document clustering provides efficient representation and visualization of newlinethe documents which helps in easy navigation. It has been studied intensively because of its wide applicability in various areas such as web mining, search engines, recommendation newlinesystem, business, biology, information retrieval, etc. The importance of document clustering emerges from the massive volume of textual documents created. Although numerous document clustering methods have been extensively studied in past years, there still exist several challenges (like high dimensionality,scalability,semantics, efficiency, etc.) for increasing the clustering quality. Particularly, most of the current documents clustering newlinealgorithms do not consider the semantic relationships which produce unsatisfactory clustering results. Recently, the efforts have been in applying semantics to document clustering. newline
Pagination: xi, 163
URI: http://hdl.handle.net/10603/209167
Appears in Departments:Department of Computer Engineering

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01_ title page.pdfAttached File35.49 kBAdobe PDFView/Open
02_ declaration by the candidate.pdf4.72 kBAdobe PDFView/Open
03_ certificate of the supervisor and guide.pdf4.98 kBAdobe PDFView/Open
04_ certificate of examining committee.pdf22.48 kBAdobe PDFView/Open
05_ contents.pdf67.4 kBAdobe PDFView/Open
06_ acknowledgement.pdf256.49 kBAdobe PDFView/Open
07_ list figures_tables.pdf187.12 kBAdobe PDFView/Open
08_ abstract.pdf46.71 kBAdobe PDFView/Open
09_ chapter 1.pdf454.6 kBAdobe PDFView/Open
10_ chapter 2.pdf1.87 MBAdobe PDFView/Open
11_ chapter 3.pdf376.15 kBAdobe PDFView/Open
12_ chapter 4.pdf595.23 kBAdobe PDFView/Open
13_ chapter 5.pdf886.98 kBAdobe PDFView/Open
14_ chapter 6.pdf585.15 kBAdobe PDFView/Open
15_chapter 7.pdf905.35 kBAdobe PDFView/Open
16_chapter 8.pdf252.07 kBAdobe PDFView/Open
17_ references.pdf263.11 kBAdobe PDFView/Open


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