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Quantitative Association Rules Mining for Business Transactional Data

http://hdl.handle.net/20.500.12678/0000003674
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f2e2d5ce-651b-4932-a432-e2c2be2682c2
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3670.pdf 3670.pdf (474 Kb)
Publication type
Article
Upload type
Publication
Title
Title Quantitative Association Rules Mining for Business Transactional Data
Language en
Publication date 2009-12-30
Authors
Nyaung, Dim En
Zaw, Wint Thida
Description
The explosive growth in data and database has generated a need for techniques and tools that can transform the processed data into useful information and knowledge that improves marketing strategy. Association rules mining is finding frequent patterns, associations, correlations, or causal structures among item sets in transaction databases, relational databases, and other information repositories. The relational tables that stored the transactions have richer attribute types such as quantitative and categorical attribute. Thus the development of tools that can extract useful information from this large database is greatly demand. This paper discusses the quantitative association rules mining from business transactional database that store the textile store. We introduce the quantitative association rules mining using with the direct application using on a real-life dataset.
Keywords
Association Rules Mining, quantitative and category attributes, quantitative association rules mining
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1415
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
283
116
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CN65
DE23
FI1
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