A Survey on Parallel Mining of frequent itemsets in MapReduce

Abstract: This paper shows the various parallel mining algorithms for frequent itemsets mining. We summarize the various algorithms that were developed for the frequent itemsets mining, like candidate key generation algorithm, such as Apriori algorithm and without candidate key generation algorithm, such as FP-growth algorithm. These algorithms lacks mechanisms like load balancing, data distribution I/O overhead, and fault tolerance. The most efficient the recent method is the FiDoop using ultrametric tree (FIUT) and Mapreduce programming model. FIUT scans the database only twice. FIUT has four advantages. First: I reduces the I/O overhead as it scans the database only twice. Second: only frequent itemsets in each transaction are inserted as nodes for compressed storage. Third: FIU is improved way to partition database, which significantly reduces the search space. Fourth: frequent itemsets are generated by checking only leaves of tree rather than traversing entire tree, which reduces the computing time.

Keywords: MapReduce, frequent itemsets, mining algorithm, ultrametric tree.