International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 6, No. 3, 8 Feb. 2014
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Efficient Analysis of Pattern and Association Rule Mining Approaches
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Author(s)
Thabet Slimani
1,*
Amor Lazzez
2
Index Terms
Association Rule, Frequent Itemset, Sequence Mining, Pattern Mining, Data Mining
Abstract
The process of data mining produces various patterns from a given data source. The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules. Numerous efficient algorithms have been proposed to do the above processes. Frequent pattern mining has been a focused topic in data mining research with a good number of references in literature and for that reason an important progress has been made, varying from performant algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining. Association Rule mining (ARM) is one of the utmost current data mining techniques designed to group objects together from large databases aiming to extract the interesting correlation and relation among huge amount of data. In this article, we provide a brief review and analysis of the current status of frequent pattern mining and discuss some promising research directions. Additionally, this paper includes a comparative study between the performance of the described approaches.
Cite This Paper
Thabet Slimani, Amor Lazzez, "Efficient Analysis of Pattern and Association Rule Mining Approaches", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.3, pp.70-81, 2014. DOI:10.5815/ijitcs.2014.03.09
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