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dc.coverage.spatialEffective performance of integrated K family clusters and decisi on tree Structures on quality assessment of Institutional dataen_US
dc.date.accessioned2015-04-10T13:08:54Z-
dc.date.available2015-04-10T13:08:54Z-
dc.date.issued2015-04-10-
dc.identifier.urihttp://hdl.handle.net/10603/38937-
dc.description.abstractThe dissertation titled Effective Performance of Integrated K family newlineClusters and Decision Tree Structures on Quality Assessment of Institutional newlineData focuses primarily on the demand for data mining in educational newlineInstitutions which is constantly going up due to the development of newlineinformation systems in this age of globalization The quality of data in newlineeducational institutions has not only immense influence but also plays a key newlinerole in technology newlineBy analyzing the reasons of low data quality on System Engineering newlineTheory a new method called data mining has been established which solves newlinethe data quality problem by a meta synthesis method that includes software newlinedesigning management and data mining, testing etc Its application shows newlinethat it has good practicality which can increase the institution s decision newlinelevels However the main objective of educational institution is to impart newlinequality education One way to reach the highest level of quality in education newlinesystems is by improving the decision making processes such as assessment newlineevaluation counseling and so on Data mining absolutely needs high quality newlinedata because no sufficient method is available to get quality data in the newlineinstitutional zone and therefore data mining and fuzzy based approach can newlinebe applied to find relationships between the attributes of student and staff and newlineget a conclusion Of course the growth of educational and institutional newlinesystems depends upon the quality of services under critical circumstances newlineThe faculty profile student performance and infrastructure requirements are newlineinstitutional performance Upon the decision making procedures such as newlineplanning counseling assessment evaluation and conformation the highest newlinelevel of quality can be attained Above all this can be achieved and utilized newlineby means of managerial and succinct decisions based upon implicit newlineknowledge The knowledge that remains hidden in the educational data set newlineand it can be extracted from data mining technology newline newlineen_US
dc.format.extentxxi, 207p.en_US
dc.languageEnglishen_US
dc.relationp197-204.en_US
dc.rightsuniversityen_US
dc.titleEffective performance of integrated K family clusters and decisi on tree Structures on quality assessment of Institutional dataen_US
dc.title.alternativeen_US
dc.creator.researcherPrakash kumar Sen_US
dc.subject.keywordClusters and Decision Treeen_US
dc.subject.keywordData miningen_US
dc.subject.keywordQuality Assessmenten_US
dc.description.noteappendix p192-196, reference p197-204.en_US
dc.contributor.guideSramaswami Ken_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.publisher.institutionFaculty of Science and Humanitiesen_US
dc.date.registeredn.d,en_US
dc.date.completed01/12/2012en_US
dc.date.awarded30/12/2012en_US
dc.format.dimensions23cm.en_US
dc.format.accompanyingmaterialNoneen_US
dc.source.universityUniversityen_US
dc.type.degreePh.D.en_US
Appears in Departments:Faculty of Science and Humanities

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01_title.pdfAttached File26.9 kBAdobe PDFView/Open
02_certificate.pdf473.46 kBAdobe PDFView/Open
03_abstract.pdf25.64 kBAdobe PDFView/Open
04_acknowledgement.pdf189.03 kBAdobe PDFView/Open
05_content.pdf50.23 kBAdobe PDFView/Open
06_chapter1.pdf1.78 MBAdobe PDFView/Open
07_chapte2.pdf2.89 MBAdobe PDFView/Open
08_chapter3.pdf3.12 MBAdobe PDFView/Open
09_chapter4.pdf4.87 MBAdobe PDFView/Open
10_chapter5.pdf4.04 MBAdobe PDFView/Open
11_chapter6.pdf8.71 MBAdobe PDFView/Open
12_chapter7.pdf405.59 kBAdobe PDFView/Open
13_appendix.pdf250.65 kBAdobe PDFView/Open
14_reference.pdf797.43 kBAdobe PDFView/Open
15_publication.pdf124.05 kBAdobe PDFView/Open
16_vitae.pdf92.7 kBAdobe PDFView/Open


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