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A Proficient Algorithm For Mining Frequent Item Sets

Frequent Item set Mining (FIM) discover recurrent item sets that are extremely associated in a transactional database. It can be used in big data applications like gene extraction, social network analysis, IOT devices sensor data analysis, etc. As the data size keeps on growing, an efficient procedure is necessary to process the enormous volume of data. Apriori is one of the topmost algorithm and its entry enhanced the research in mining. The algorithm requires multiple scans of the database and produce more candidate items, which increase the computation time and storage along with the transactions size. An efficient technique is required to boost the performance of the algorithm in terms of storage and computational complexity. To reduce the complexity we proposed a reduction factor to Apriori and it can be adopted before candidate generation. Experimental results revealed that our proposed technique greatly reduce the total execution time as well as storage requirements.