Abstract: Frequent pattern mining has become an important data mining task and has been a focused theme in data mining research. Frequent patterns are patterns that appear in a data set frequently. Frequent pattern mining searches for recurring relationship in a given data set. Various techniques have been proposed to improve the performance of frequent pattern mining algorithms. This paper presents review of different frequent mining techniques including apriori based algorithms, partition based algorithms, DFS and hybrid algorithms, pattern based algorithms, SQL based algorithms and Incremental apriori based algorithms. A brief description of each technique has been provided. In the last, different frequent pattern mining techniques are compared based on various parameters of importance. Experimental results show that FP- Tree based approach achieves better performance.

Deepak Garg and Hemant Sharma, “Comparative Analysis of Various Approaches Used in Frequent Pattern Mining” International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence, 2011. http://dx.doi.org/10.14569/SpecialIssue.2011.010323

@article{Garg2011,
title = {Comparative Analysis of Various Approaches Used in Frequent Pattern Mining},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence}
doi = {10.14569/SpecialIssue.2011.010323},
url = {http://dx.doi.org/10.14569/SpecialIssue.2011.010323},
year = {2011},
publisher = {The Science and Information Organization},
volume = {1},
number = {3},
author = {Deepak Garg and Hemant Sharma},
}