Frequent Pattern Mining Algorithm on MapReduce Model: A Review

Abstract- Frequent Itemset Mining (FIM) is one of the most well-known method to use in extract knowledge from data. FIM methods becomes more problematic when it is applied to huge amount of dataset like Big Data. There are many algorithms available for Frequent Pattern Mining. Existing algorithms has many problem when mining huge amount of datasets such as expensive because of frequently I/O disk scans, huge calculation on candidate generation and it does not support large amount of data. To overcome these problem or for scalable implementation of algorithm. MapReduce programming model is used, which is available for huge amount of data.Hadoop provide an open source platform for implementation of MapReduce.In this paper, we explore some current frequent itemset mining algorithm on MapReduce model and study their challenges and limitations