ABSTRACT
Big data in a distributed computational environment is the need of digital era, which requires analysis, monitoring, and control. In the present scenario of big data, space, and time complexity have raised out new challenges to frequent itemsets mining. The key to success of MapReduce framework is appropriate for distributed data processing on commodity nodes. Apriori algorithm is one of the most popular and widely used data mining algorithm. Which is applied to produce frequent itemsets. The present paper deals with the overview of several procedures, which is on parallel platform to improve the performance of the traditional Apriori algorithm. It also represents the advantages of MapReduce framework. And explore the new path of research in the future.
