Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/27643
Title: Studies in multi relational extensions to rough set theory
Researcher: Sakaya milton R
Guide(s): Arul siromoney
Keywords: information and communication engineering
multi relational extensions
rough set theory
Upload Date: 10-Nov-2014
University: Anna University
Completed Date: 01/10/2007
Abstract: Current hardware and database technology allows efficient and inexpensive newlinestorage and access of vast amounts of data Data mining algorithms newlinesearch for patterns that may exist but are hidden among the vast amounts newlineof data One of the functions of data mining is classificationclassifying data newlineobjects examples into one of the predefined classes newlineRough Set Theory is one of the frameworks for classification of examples newlineusing inconsistent or incomplete information The Variable Precision newlineRough Set VPRS model is a generalized model of rough sets and allows a newlinecontrolled degree of uncertainty in the classification newlineThe limitation of the rough set model is that it analyzes data represented newlinein a single table Relational learning is learning from data represented in newlinemultiple tables newline newline newline
Pagination: xix, 161p.
URI: http://hdl.handle.net/10603/27643
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File28.73 kBAdobe PDFView/Open
02_certificate.pdf71.96 kBAdobe PDFView/Open
03_abstract.pdf22.33 kBAdobe PDFView/Open
04_acknowledgement.pdf19.36 kBAdobe PDFView/Open
05_contents.pdf91.22 kBAdobe PDFView/Open
06_chapter 1.pdf695.42 kBAdobe PDFView/Open
07_chapter 2.pdf447.99 kBAdobe PDFView/Open
08_chapter 3.pdf497.3 kBAdobe PDFView/Open
09_chapter 4.pdf506.01 kBAdobe PDFView/Open
10_chapter 5.pdf722.81 kBAdobe PDFView/Open
11_chapter 6.pdf47.06 kBAdobe PDFView/Open
12_references.pdf74.87 kBAdobe PDFView/Open
13_publications.pdf21.05 kBAdobe PDFView/Open
14_vitae.pdf16.88 kBAdobe PDFView/Open


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