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Dagon University
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Kyaukse University
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Laquarware Technological college
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Mandalay Technological University
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Mandalay University of Distance Education
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Maubin University
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Mawlamyine University
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Meiktila University
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Mohnyin University
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Myanmar Institute of Information Technology
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Myanmar Maritime University
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National Management Degree College
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Naypyitaw State Academy
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Pathein University
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Sagaing University
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Sagaing University of Education
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Taunggyi University
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Technological University, Hmawbi
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Technological University (Kyaukse)
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Technological University Mandalay
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University of Computer Studies, Mandalay
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University of Computer Studies Maubin
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University of Computer Studies, Meikhtila
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University of Computer Studies Pathein
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University of Computer Studies, Taungoo
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University of Computer Studies, Yangon
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University of Dental Medicine Mandalay
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University of Dental Medicine, Yangon
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University of Information Technology
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University of Mandalay
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University of Medicine 1
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University of Medicine 2
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University of Medicine Mandalay
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University of Myitkyina
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University of Public Health, Yangon
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University of Veterinary Science
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West Yangon University
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Yadanabon University
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Yangon Technological University
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Yangon University of Distance Education
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Yangon University of Economics
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Yangon University of Education
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Yangon University of Foreign Languages
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Yezin Agricultural University
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New Index
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Item
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Proposed Method in Adoptive Frequent Itemset Generation
http://hdl.handle.net/20.500.12678/0000004634
http://hdl.handle.net/20.500.12678/0000004634258660cf-bcbe-41ef-a27c-65e5bc18e9f0
b995c0df-6086-4969-8c17-d73811db704a
Publication type | ||||||
---|---|---|---|---|---|---|
Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Proposed Method in Adoptive Frequent Itemset Generation | |||||
Language | en | |||||
Publication date | 2018-02-22 | |||||
Authors | ||||||
Yu, Thanda Tin | ||||||
Lynn, Khin Thidar | ||||||
Description | ||||||
Apriori is an algorithm for frequent item set mining and association rule mining over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. Frequent item set mining and association rule induction are powerful methods for application in domains such as in the shopping behavior of customers of supermarkets, mail-order companies, online shops etc. Firstly, we check if the items are greater than or equal to the minimum support and find the frequent itemsets respectively. Then, the minimum confidence is used to form association rule. This paper proposed the new algorithm based on Apriori algorithm. In this new algorithm, it can reduce the computational complexity than Apriori algorithm. So, the processing time is faster. And it can be used in any dataset which is executable with Apriori algorithm. | ||||||
Keywords | ||||||
Data Mining, Apriori algorithm, Frequent Pattern mining, Adaptvie Apriori algorithm | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/273 | |||||
Journal articles | ||||||
Sixteenth International Conferences on Computer Applications(ICCA 2018) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |