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A Novel Association Rule Algorithm to Discover Maximal Frequent Item Set
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 137 - Number 9 |
| Year of Publication: 2016 |
| Authors: Hartej Singh, Vinay Dwivedi |
10.5120/ijca2016908883
|
Hartej Singh, Vinay Dwivedi . A Novel Association Rule Algorithm to Discover Maximal Frequent Item Set. International Journal of Computer Applications. 137, 9 ( March 2016), 1-4. DOI=10.5120/ijca2016908883
Abstract
Association Rule mining is a sub-discipline of data mining. Apriori algorithm is one of the most popular association rule mining technique. Apriori technique has a disadvantage that before generating a maximal frequent set it generates all possible proper subsets of maximal set. Therefore it is very slow as it requires many database scans before generating a maximal frequent itemset In the method proposed in this paper entire database is scanned only once. Frequency count of all distinct transactions is stored in a hash map. Algorithm maintains an array of tables such that each table in the array contain frequency count of all potential k-itemsets..Binary search and the concept of longest common subsequence are used to efficiently extract maximal frequent itemset. Experimental results show that proposed algorithm performs better than apriori algorithm.
References
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