Parallel Analytical Model for Frequent Itemset Mining

The data generated in real world applications in recent days are quite huge and processing and analyzing those data has become a challenging task. For any analytical and inference engine finding frequent itemsets turns out to be a major functionality. Frequent itemset mining is performed usually with the help of association rule mining technique in data mining. Generally the results obtain from these techniques are large and diverse which makes it difficult for an inference engine to reach a conclusion. One of the ideal solutions for handling such datasets is by devising a parallel processing system to efficiently run the data mining approaches and obtain a more accurate and easily analyzable output. The research carried out here aims at designing and developing parallel analytical model for frequent itemset mining in big data by integrating R on Hadoop.