Comparing and identifying common factors in frequent item set algorithms in association rule

This paper is initiated from the observation of existing research work which is related in frequent Item Set mining algorithms such as MAFIA, FP -Growth, Transaction Mapping (TM) and ECLAT(Equivalence CLAss Transformation). As per the study of above mentioned algorithms all the items are counted then its maximal sets are reordered separately. The algorithms are executed with the limitation of candidate key generation and the candidate keys are generated after the frequent item set generation. The common features are identified. As per the observation, the three common factors total processing time, total number of transactions and dataset scanning and accessibility are taken. The results are compared and critically commented in this paper.