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  1. University of Computer Studies, Yangon
  2. Conferences

Mining Association Rule by ECLAT Method Using Transaction Data

http://hdl.handle.net/20.500.12678/0000003705
http://hdl.handle.net/20.500.12678/0000003705
ada611c8-3ded-4b34-9344-fba5d9c7aef1
3e0e454e-e476-4b0e-8791-f5f2c807be1f
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54015.pdf 54015.pdf (274 Kb)
Publication type
Article
Upload type
Publication
Title
Title Mining Association Rule by ECLAT Method Using Transaction Data
Language en
Publication date 2009-12-30
Authors
Mon, Pan Myat
Renu
Oo, Thet Lwin
Description
Association rule mining is a process that identifies links between sets of correlated objects in transactional databases where each transaction contains a list of items. Association rule is one of the well-defined algorithms, whose significance is measured via support and confidence factor, are intended to identify rules of the type. This system is the development of transactions data analysis system. The important problems of data mining are mining frequent itemsets and generating association rules from databases of transactions where each transaction consists of a set of items. Our proposed system is based on Association Rule Mining using Equivalence CLASS Transformation (ECLAT) method to find frequent-patterns. This method can also reduce the number of candidate itemsets. It is not required scanning the complete database over and over again. So, it also saves the time.
Keywords
Data Mining, Association Rules Mining (ARM), Frequent-pattern Mining Algorithm, Performance Improvements, Knowledge Discovery in Database (KDD)
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1443
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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