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Frequent Itemset Mining in Data Mining: A Survey

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Rana Ishita, Amit Rathod
10.5120/ijca2016909219

Rana Ishita and Amit Rathod. Article: Frequent Itemset Mining in Data Mining: A Survey. International Journal of Computer Applications 139(9):15-18, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Rana Ishita and Amit Rathod},
	title = {Article: Frequent Itemset Mining in Data Mining: A Survey},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {9},
	pages = {15-18},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Data mining is process of extracting useful information from different perspectives. Frequent Item set mining is widely used in financial, retail and telecommunication industry. The major concern of these industries is faster processing of a very large amount of data. Frequent item sets are those items which are frequently occurred. So we can use different types of algorithms for this purpose. Frequent Item set mining can be performed Apriori, FP-tree, Eclat, and RARM algorithms. For the work in this paper, we have analyzed widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori, Frequent Pattern (FP) Growth, Rapid Association Rule Mining (RARM) and ECLAT algorithm frequent pattern mining algorithms. This study also focuses on each of the algorithm’s advantages, disadvantages and limitations for finding patterns among large item sets in database systems.

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Keywords

Association rule, Frequent Item, Data Mining