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Association mining is one of the most researched areas of data mining and has received much attention from the database community. Association rules are interesting correlations among attributes in a database. These rules have many applications in areas ranging from e-commerce to sports to census analysis to medical diagnosis. The most time consuming operation in discovery of association rules is computation of frequency of interesting subset of items (called candidates) in the database of transactions. Hence, it has become vital to develop a method that may avoid or reduce candidate generation and test, utilize some novel data structures to reduce the cost in frequent pattern mining. An Effectual Generalized Mesh Transposition Algorithm (EGMTA) is proposed which is an integrated approach of Parallel Computing and ARM for mining Association Rules in Generalized data set. EGMTA is fundamentally different from all the previous algorithms. As EGMTA uses database in transposed form which has been done using Parallel transposition (Mesh Transpose), hence to generate all significant association rules number of passes required is reduced.