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Frequent Pattern Mining based on Multiple Minimum Support using Uncertain Dataset

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 99 - Number 6
Year of Publication: 2014
Authors:
Meenu Dave
Hitesh Maharwal
10.5120/17377-7913

Meenu Dave and Hitesh Maharwal. Article: Frequent Pattern Mining based on Multiple Minimum Support using Uncertain Dataset. International Journal of Computer Applications 99(6):20-23, August 2014. Full text available. BibTeX

@article{key:article,
	author = {Meenu Dave and Hitesh Maharwal},
	title = {Article: Frequent Pattern Mining based on Multiple Minimum Support using Uncertain Dataset},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {99},
	number = {6},
	pages = {20-23},
	month = {August},
	note = {Full text available}
}

Abstract

Association rule mining plays a major role in decision making in the production and sales business area. It uses minimum support (minsup) and support confidence (supconf) as a base to generate the frequent patterns and strong association rules. Setting a single value of minsup for a transaction set doesn't seem feasible for some real life applications. Similarly the probabilistic value of items in the transaction set may be acceptable. So generating the frequent pattern from the uncertain dataset becomes a concern factor. This research work details the aforesaid problem and proposes a solution for the same.

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