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Frequent Item Set Mining using Association Rules

Amritpal Kaur, Vaishali Aggarwal. Published in Information Sciences.

Communications on Applied Electronics
Year of Publication: 2015
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Amritpal Kaur, Vaishali Aggarwal
10.5120/cae2015651945

Amritpal Kaur and Vaishali Aggarwal. Article: Frequent Item Set Mining using Association Rules. Communications on Applied Electronics 3(5):18-20, November 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Amritpal Kaur and Vaishali Aggarwal},
	title = {Article: Frequent Item Set Mining using Association Rules},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {3},
	number = {5},
	pages = {18-20},
	month = {November},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

One of the most difficult tasks in data mining is to fetch the frequent item set from large database. Related to this many conquering algorithms have been introduced till now. Whereas frequent item set figures out pattern, correlation as well as association between items in a bulky database and these constraints provides better scope in mining process. During study it has been founded that either support count or candidate count are been taken into consideration by using less and strong association rules. But this approach doesn’t improve time parameter (which seems to be constant). Our proposed work is based on reducing time component by considering both pre and post processing results in each transaction. As a result frequent item set will be formed by applying further strong association rules with the help of pre defined support and candidate count. In result this approach will acquire better performance.

References

  1. Basheer Mohamad Al-Maqaleh, S. K. (2013). An Efficient Algorithm for Mining Association Rules using Confident Frequent Itemsets.
  2. Cesario Eugenio, Carlo Mastroianni, Domenico Talia. (2014). A Multi-Domain Architecture for Mining Frequent Items and Itemsets from Distributed Data Streams.
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  8. XuePing Zhang, Y. Z. (2010). Improved Parallel Algorithm for Mining Frequent Item-set Used in HRM.
  9. Yen-hui Liang, S.-y. W. (2015). Sequence -Growth : A Scalable and Effective Frequent Itemset Mining Algorithm for Big Data Based on MapReduce Framework.

Keywords

Data mining; pre-processing; post-processing; confidence count; support count; association rules;