Potential item set mining by using utility pattern growth model in big data

Abstract

Information extracted by using data mining in earlier days. Now a day’s, the most talked about technology is Big Data. Utility Mining is the most crucial task in the real time application where the customers prefer to choose the item set which can yield more profit. Handling of large volume of transactional patterns becomes the complex issue in every application which is resolved in the existing work introducing the parallel utility mining process which will process the candidate item sets in the paralyzed manner by dividing the entire tasks into sub partition. Each sub partition would be processed in individual mapper and then be resulted with the final output value. The time complexity would be more when processing an unnecessary candidate item sets. This problem is resolved in the proposed methodology by introducing the novel approach called UP-Growth and UP-Growth+ which will prune the candidate item sets to reduce the dimension of the candidate item sets. The time complexity is further reduced by representing the candidate item sets in the tree layout. The test results prove that the proposed new approach provides better result than the existing work in terms of accuracy.