Pruning Large Data Sets for Finding Association rule in cloud: CBPA (Count Based Pruning Algorithm )

Organizations are more interested in the interesting data rather than the bulk of data. So they need a systematic and scientific approach to extract meaningful data out of heaps of the data and to find out the relations among these patterns. To analyze "big data" on clouds, it is very important to research data mining strategies based on cloud computing paradigm from both theoretical and practical views. In this paper, based on the original Apriori algorithm, an improved algorithm is proposed which adopts a new count-based method to prune candidate item sets and uses generation record to reduce total data scan amount and also make it more modeling oriented. Experiments demonstrate that by performing our algorithm of on given datasets we will find the Solution of problems in association and apriori algorithm.