An efficient approach for generating frequent patterns without candidate generation
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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There are various algorithm for finding frequent itemsets one of them is the Association rule mining. Association rule mining uses an algorithm called apriori to find frequent itemsets. But due to some limitations viz. producing large number of candidate itemsets, which results in frequent database scanning while finding frequent itemsets. For solution of all these drawbacks, here new algorithm is introduced named, Modified RAAT (Reduced Apriori Algorithm using Tag). Modified RAAT is more efficient because it performs reduction in database scan time by using a special feature named Tag parameter, A Tag parameter includes three more parameters: minimum, maximum and total number of items in a particular transaction. One can conclude the following by Comparing this Tag value with a transaction containing particular itemset, Mismatching of the parameters requires no further usage of that transaction. By counting the change in support parameter the Modified RAAT algorithm may also find various emerging patterns like JEP (jumping emerging pattern). The pattern whose support changes abruptly from zero to nonzero is classified as JEP. This concept is designed by using border-based approach to find most expressive JEP.