Eclat Algorithm for FIM on CPU-GPU co-operative & parallel environment

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Extracting the frequent itemsets from a transactional database is a fundamental task in data mining field because of its broad applications in mining association rules, time series, correlations etc. The Apriori or Eclat approaches are the commonly used generate-and-check approach to obtain frequent itemsets from a database with a given threshold value. Implementations take advantage of the GPU's massively multi-threaded SIMD (Single Instruction, Multiple Data) architecture which will employ a bitmap data structure to represent vertical transaction list ,to exploit the GPU's SIMD parallelism , and to perform the support counting operation. The implementation runs entirely on the GPU and eliminates intermediate data transfer between the GPU memory and the CPU memory, which can reduce computation time and improve overall performance .OpenCL is a platform independent Open Computing Language for GPU computation. Thus, the aim of our approach is to develop efficient parallel new advanced Eclat strategy of Frequent Itemset Mining that utilize new-generation graphics processing units (GPUs) to speed-up the process.