Mining Association Rule by ECLAT Method Using Transaction Data

Mon, Pan Myat; Renu; Oo, Thet Lwin

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Mining Association Rule by ECLAT Method Using Transaction Data

http://hdl.handle.net/20.500.12678/0000003705

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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