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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 |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 202.83 kB | Adobe PDF | View/Open |
02_declaration.pdf | 1.93 MB | Adobe PDF | View/Open | |
03_certificate.pdf | 17.81 MB | Adobe PDF | View/Open | |
06_list of graph and table.pdf | 95.16 kB | Adobe PDF | View/Open | |
07_chapter 1.pdf | 16.18 MB | Adobe PDF | View/Open | |
08_chapter 2.pdf | 14.59 MB | Adobe PDF | View/Open | |
09_chapter 3.pdf | 28.33 MB | Adobe PDF | View/Open | |
10_chapter 4.pdf | 14.21 MB | Adobe PDF | View/Open | |
10_chapter 5 a.pdf | 14.74 MB | Adobe PDF | View/Open | |
10_chapter 6 b.pdf | 4.26 MB | Adobe PDF | View/Open | |
10_chapter 7 c.pdf | 9.34 MB | Adobe PDF | View/Open | |
10_chapter 8 d.pdf | 17.04 MB | Adobe PDF | View/Open | |
10_chapter 9 e.pdf | 99.94 kB | Adobe PDF | View/Open | |
11_bibliography.pdf | 274.28 kB | Adobe PDF | View/Open | |
12_annexure.pdf | 72.04 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 81.05 kB | Adobe PDF | View/Open | |
abbreviation.pdf | 62.37 kB | Adobe PDF | View/Open | |
abstract.pdf | 81.05 kB | Adobe PDF | View/Open | |
preliminary page.pdf | 202.83 kB | Adobe PDF | View/Open |
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