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Association Rules in data mining are generated by identifying relationships among set of items in transaction database. Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. Although several techniques have emerged, they are all inherently dependent on the memory availability. This paper describes an efficient algorithmic approach called Bit Stream Mask Search which sorts the transaction database by transforming to numeric attributes. In the next step, frequent itemsets are found out, algorithms generated and the data hidden during the process time. During the search process, Masked Itemset Processing (MIP) searches the itemsets with a low execution time.
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