Mining of frequent itemsets from large databases has been an interesting area for data miners from the beginning of data mining research. Knowing frequent patterns, data miners can determine interesting relationships among the items. In the proposed work, the original database is scanned once and the encoded database transactions are stored as a matrix. All frequent patterns are then determined from this matrix of coded transactions. An efficient algorithm has been developed to mine all frequent itemsets directly from this encoded matrix with the help of a reference matrix. The proposed approach reduces the memory size required for the database and the number of database scans to one. The algorithm finds its application in distributed data mining and secure data publishing.

Mining Frequent Pattern, Matrix Approach, Reference Matrix, Compressed Database, Market Basket Analysis, Apriori Algorithm

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