Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/376495
Title: A Study of Various Pattern Mining Techniques for Selected Domains
Researcher: Rajpoot, Devendra Singh
Guide(s): SAXENA, KANAK AND KHARE, ANUBHUTI
Keywords: Computer Science
Computer Science Information Systems
Engineering and Technology
University: Rajiv Gandhi Proudyogiki Vishwavidyalaya
Completed Date: 2012
Abstract: We are trying to use data mining techniques in such a manner so that we will be able to learn patterns of large database in such a way to understand the effect of changing or modifying in the present database newlineas per there pattern learning into the future. It is impossible to propose newlinethe future effect of any changes in present data. However, there are important new issues which arise because of the sheer size of the data. One of the important problems in data mining is the pattern behaviour newlinelearning which involves finding rules that partition given data into various data sets. In the data mining domain where millions of records and a large number of attributes are involved, the execution time of existing standard algorithms can become prohibitive; particularly in interactive applications, we are trying to learn patterns of different newlineresult data in university domain. newlineIn general, we are unable to decide how we should justify a change in syllabus of any course until unless we have not seen the newlineeffect, which is also difficult because of not keeping an eye on the growth of the students which resultant in the overall career of the student. Today, the general criteria are the only market analysis even in newlinethe field of education that this course is required with these contents without any technical aspect to have the change or not, whether it is must for any student of that discipline and caliber. This research support to do complete analysis in all aspects whether it is market, education, necessity, caliber, drop down, etc. This research furnishes the strong rules in terms of patterns which may be supervised or semi supervised followed by the unsupervised learning growth pattern. This research will not only sustain the university newlineworking with technical grounds but also improves the students behaviour pattern. For this, we are using the data mining techniques support. The process that is adopted in this research work is preprocess, pattern discovery, pattern analysis and predict students behaviour.The major problem face
Pagination: 136MB
URI: http://hdl.handle.net/10603/376495
Appears in Departments:Department of Computer Applications

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01_title.pdfAttached File202.83 kBAdobe PDFView/Open
02_declaration.pdf1.93 MBAdobe PDFView/Open
03_certificate.pdf17.81 MBAdobe PDFView/Open
06_list of graph and table.pdf95.16 kBAdobe PDFView/Open
07_chapter 1.pdf16.18 MBAdobe PDFView/Open
08_chapter 2.pdf14.59 MBAdobe PDFView/Open
09_chapter 3.pdf28.33 MBAdobe PDFView/Open
10_chapter 4.pdf14.21 MBAdobe PDFView/Open
10_chapter 5 a.pdf14.74 MBAdobe PDFView/Open
10_chapter 6 b.pdf4.26 MBAdobe PDFView/Open
10_chapter 7 c.pdf9.34 MBAdobe PDFView/Open
10_chapter 8 d.pdf17.04 MBAdobe PDFView/Open
10_chapter 9 e.pdf99.94 kBAdobe PDFView/Open
11_bibliography.pdf274.28 kBAdobe PDFView/Open
12_annexure.pdf72.04 kBAdobe PDFView/Open
80_recommendation.pdf81.05 kBAdobe PDFView/Open
abbreviation.pdf62.37 kBAdobe PDFView/Open
abstract.pdf81.05 kBAdobe PDFView/Open
preliminary page.pdf202.83 kBAdobe PDFView/Open
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