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

Abstract

Abstract-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. I. INTRODUCTION Frequent itemset mining is a noteworthy research subject in associations, correlations, classification, sequences and other essential data mining tasks. To find out frequent item sets is one of the basic computational task in association rule mining where Frequent Item-set is gathering of similar items that happens together in numerous transactions. In association rule mining, to discover Frequent Itemset , characterizes the two similar itemsets in which first itemsets has similar itemsets of another. These rules are helpful for finding interesting relationships in the datasets and gives insight to the procedure that generated the data [12]. Now a days there are various information creates from different sources like IT enterprises, administrations, advancements and information. These large information is available with different structures. To deal with such excessive information is exceptionally troublesome because it has millions of transactions of users, products etc. There are number of strategies to discover frequent itemsets from database. These techniques function well on usual datasets, however not appropriate on excessive amount of data. To utilize frequent itemset mining strategy on massive database is very critical task. To accelerate the procedure of FIM is complex and indispensable, because FIM consumes vast significant portion of time to do high computation and input/output intensity. One of the solution to this issue is to use a new parallel frequent itemsets mining algorithm with MapReduce called as FIMMA (Frequent Itemsets Mining with Modified Apriori) [12]. In this modern era datasets are very large so only ...

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“…TaskTracker. The task scheduler is mainly used to coordinate specific calculations and responds to the Job-Tracker with information such as the time spent in the calculation process, the number of processing tasks, the occupied CPU, and memory, and at the same time, it processes the assigned tasks [13,14]. Map and reduce are coordinated to complete.…”