Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data process. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, present improvements in the field of parallel programming already provide good tools to tackle this problem. However, these tools come with their own technical challenges, e.g. balanced data distribution and inter-communication costs. In this paper, we analysis the applicability of FIM techniques on the MapReduce platform. In this paper propose a Confabulation Base Parallel FIM approach called CBP-FIM-DP using the MapReduce programming model. The above mentioned FIM mining algorithms extract from and analyze the historical datasets for decision making. The purpose of Big data mining is to go beyond the usual request-response processing, market basket analysis or uncovering some hidden relationships and implement very large scale parallel data mining algorithm. Comparing with the results derived from mining the conventional datasets, unveiling the huge volume of interconnected heterogeneous big data has the potential to maximize our knowledge in the target domain. In our experiments we show the scalability of our methods.
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