Abstract
Frequent itemset extraction is a very important task in data mining applications. This is useful in applications like Association rule mining and co-relations. They are using some algorithms to extract the frequent itemsets, like Apriori and FP-Growth. The algorithms used by these applications are inefficient to support balancing, distributing the load, and automatic parallelization with good speed. Data partitioning and fault tolerance is also not possible because of excessive data. Hence, there is a need to develop algorithms which will remove these issues. Here, a novel approach is used to work on the extracting the frequent itemsets using MapReduce. This system is based on the Modified Apriori, called as Frequent Itemset Mining using Modified Apriori(FIMMA). To automate the data parallelization, well balance the load and to reduce the execution time FIMMA works concurrently and independently using three mappers. It uses decomposing strategy to work concurrently.
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