An Improved Technique Of Extracting Frequent Itemsets From Massive Data Using MapReduce

The mining of frequent itemsets is a basic and essential work in many data mining applications. Frequent itemsets extraction with frequent pattern and rules boosts the applications like Association rule mining, co-relations also in product sale and marketing. In extraction process of frequent itemsets there are number of algorithms used Like FP-growth,E-clat etc. But unfortunately these algorithm are inefficient in distributing and balancing the load, when it come across massive data. Automatic parallelization is also not possible with these algorithms. To defeat these issues of existing algorithms there is need to construct an algorithm which will support the missing features, such as automatically parallelization, balancing and good distribution of data. This paper is focusing on a efficient methodology to extract frequent itemsets with the popular MapReduce approach. This new methodology consist an algorithm which is build using Modified Apriori algorithm,called as Frequent Itemset Mining using Modified Apriori (FIMMA) Technique. This methodology works with three mappers, independently and concurrently by using the decompose strategy. The result of these mappers will be given to the reducers using the hash table method. Reducers gives the top most frequent itemsets. Keyword-Association Rules, Frequent item sets, Load balancing, MapReduce, Modified Apriori, FIMMA.