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Kyaukse 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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Naypyitaw State Academy
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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, 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 Medicine 1
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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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Finding Frequent Itemsets of Healthy Shop Products Using APRIORI Algorithm
http://hdl.handle.net/20.500.12678/0000003760
http://hdl.handle.net/20.500.12678/0000003760d5b1613a-860e-4bd7-8c06-60eec5a226c8
1ce511d4-7236-46d4-a2b1-4045fc5ec77a
Name / File | License | Actions |
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Publication type | ||||||
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Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Finding Frequent Itemsets of Healthy Shop Products Using APRIORI Algorithm | |||||
Language | en | |||||
Publication date | 2009-12-30 | |||||
Authors | ||||||
Khaing, Ngwe Zin | ||||||
Sandar, Khin | ||||||
Oo, May Phyo | ||||||
Description | ||||||
Data mining in an area in the intersection of machine learning statistics, and database is to use searching for relationships and global pattern that exits, but is hidden in large database. The discovery of interesting association relationships among huge amount of business transaction records can help in many business decision making processes, such as catalog design, cross marketing and loss leader analysis. This paper intends to an effective data mining process that contains the concept of market basket analysis using association rule mining. Apriori is an influential algorithm for mining frequent itemsets under Boolean association rules. The name of the algorithm is based on the fact that the algorithm uses prior knowledge of frequent itemsets properties. This system analyzes the customer buying habits of healthy shop products and finding associations between the different items that customer places in their shopping baskets. And their rules are generating from frequent item sets based on the healthy shop transaction data. The result is Market Basket Analysis in web interface that made easier to navigate and visualize the data. | ||||||
Keywords | ||||||
Aprior Algorithm, Frequent Itemsets, Association Rule, Primary/Foreign Keys, finding frequent itemsets mining | ||||||
Identifier | http://ucsy.edu.mm/onlineresource/handle/123456789/1493 | |||||
Journal articles | ||||||
Fourth Local Conference on Parallel and Soft Computing | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |