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Performance Evaluation of some Online Association Rule Mining Algorithms for sorted and unsorted Data sets

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International Journal of Computer Applications
© 2010 by IJCA Journal
Number 6 - Article 7
Year of Publication: 2010
Authors:
Pramod S.
O.P.Vyas
10.5120/670-941

Pramod S. and O.P.Vyas. Article:Performance Evaluation of some Online Association Rule Mining Algorithms for sorted and unsorted Data sets. International Journal of Computer Applications 2(6):40–45, June 2010. Published By Foundation of Computer Science. BibTeX

@article{key:article,
	author = {Pramod S. and O.P.Vyas},
	title = {Article:Performance Evaluation of some Online Association Rule Mining Algorithms for sorted and unsorted Data sets},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {2},
	number = {6},
	pages = {40--45},
	month = {June},
	note = {Published By Foundation of Computer Science}
}

Abstract

The association rules and its usage put forwarded lots of hopes in the field of data mining. The researchers in the field are going after the association rule mining techniques to find fastest as well as more precise association rules so that it will indirectly increase the profit of the company if we take it as an example. Here in this paper our effort is to do the performance evaluation of some of the existing association rule mining algorithms. Now a day’s, online association rule mining is getting its importance due to the popularity of internet as well as the changing behavior of the customer to depend internet for almost everything. The time required for generating frequent itemsets plays an important role. Some algorithms are designed, considering only the time factor. The implementations has been tested as by used the dataset from Freequent Itemset Mining(FIM) Dataset repository. The work yields a detailed analysis with deep understanding of the algorithms to elucidate the performance with standard datasets. The performance evaluation includes aspects like different support value, size of transaction and different datasets.

Reference

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