Big data is a collection of large amount of data with various types of data and usable to be processed at much higher frequency. Frequent Itemset Mining is one of the classical data mining problems in most of the data mining applications in big data era. In data mining, association rule mining is key technique for discovering useful patterns from large collection of data. Frequent itemset mining is a famous step of association rule mining. Many efficient pattern mining algorithms have been discovered in the last two decades, yet most do not hold good for Big Dataset. In association rule mining (ARM) a Frequent Itemset Mining (FIM) is a well-known step. In last two decades, many efficient pattern mining algorithms have been discovered, up till now most do not hold good for Big Dataset. The Apriori, FP-growth and Eclat algorithms are the most famous algorithms which can be used for Frequent Pattern mining. However, these parallel mining algorithms lack features like automated parallelization, fine load balancing, and distribution of data on large clusters. To overcome these problems various parallelized approaches using Hadoop MapReduce model are developed to perform frequent itemsets mining from big data. This paper gives overall study about frequent pattern mining in big data.

Big data, Pattern Mining, Frequent Itemset Mining, Data Mining, ItemSets

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