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A new algorithm for mining maximal frequent items was proposed in this paper. The proposed algorithm initially discover the missing items in the transactions and then uses simple bit vector computations to unearth maximal frequent itemsets from the large transactional datasets. The noticeable attribute of the proposed algorithm MAX-MiBit is that the maximal frequent itemsets are discovered in a straight forward method and the uncertain items are removed instantaneously to enhance the execution speed and to decrease the memory consumption. The experimental evaluation showcased that the proposed algorithm MAX-MiBit outscores the state of the art current algorithms by a huge margin with respect to execution speed and memory consumption. Keywords-Frequent Itemset,Bitvector,Maximal,Datamining. 1.INTRODUCTION Currently there are many algorithms which produce frequent itemsets efficiently. However, a drawback of these algorithms is that they may discover too many frequent patterns to users. A very large number of frequent patterns make it difficult for users to analyze results to gain insightful knowledge. It may also cause the algorithms to become inefficient in terms of time and memory because the more frequent patterns the algorithms produce, the more resources they consume. The problem becomes worse when the database contains long frequent patterns. A frequent itemset is nothing but that occurs in user-defined times in the transaction database. That number of time or occurrence is called minimum support value of the item. An itemset is considered to be closed if its immediate supersets do not contain the same minimum support. An itemset is considered as maximal frequent if its immediate supersets are not frequent.

MAX-MiBit-An Algorithm To Discover Maximal Frequent Itemsets From Large Transactional Datasets