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Frequent Itemset Mining in Data Mining: A Survey
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 139 - Number 9 |
| Year of Publication: 2016 |
| Authors: Rana Ishita, Amit Rathod |
10.5120/ijca2016909219
|
Rana Ishita, Amit Rathod . Frequent Itemset Mining in Data Mining: A Survey. International Journal of Computer Applications. 139, 9 ( April 2016), 15-18. DOI=10.5120/ijca2016909219
Abstract
Data mining is process of extracting useful information from different perspectives. Frequent Item set mining is widely used in financial, retail and telecommunication industry. The major concern of these industries is faster processing of a very large amount of data. Frequent item sets are those items which are frequently occurred. So we can use different types of algorithms for this purpose. Frequent Item set mining can be performed Apriori, FP-tree, Eclat, and RARM algorithms. For the work in this paper, we have analyzed widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori, Frequent Pattern (FP) Growth, Rapid Association Rule Mining (RARM) and ECLAT algorithm frequent pattern mining algorithms. This study also focuses on each of the algorithm’s advantages, disadvantages and limitations for finding patterns among large item sets in database systems.
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