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Quantitative Association Rules Mining for Business Transactional Data

http://hdl.handle.net/20.500.12678/0000002911
8fef0a2a-59dc-4358-85d2-6ab128b9d77d
6f890d5c-02f5-42dc-bced-e450ede9d6da
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Quantitative Quantitative Association Rules Mining for Business Transactional Data.pdf (474 Kb)
Publication type
Conference paper
Upload type
Publication
Title
Title Quantitative Association Rules Mining for Business Transactional Data
Language en
Publication date 2009-12-01
Authors
Dim En Nyaung
Wint Thida Zaw
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
Conference papers
PSC
1 December, 2009
The Fourth Local Conference On Parallel and Soft Computing
University of Computer Studies, Yangon (UCSY), Myanmar
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