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Data mining is wide spreading its applications in several areas. There aredifferent tasks in mining which provides solutions for wide variety of problemsin order to discover knowledge. Among those tasks association mining plays apivotal role for identifying frequent patterns. Among the available associationmining algorithms Apriori algorithm is one of the most prevalent and dominantalgorithm which is used to discover frequent patterns. This algorithm is usedto discover frequent patterns from small to large databases. This paper pointstoward the inadequacy of the tangible Apriori algorithm of wasting time forscanning the whole transactional database for discovering association rules andproposes an enhancement on Apriori algorithm to overcome this problem. Thisenhancement is obtained by dropping the amount of time used in scanning thetransactional database by just limiting the number of transactions whilecalculating the frequency of an item or item-pairs. This improved version ofApriori algorithm optimizes the time used for scanning the whole transactionaldatabase.

An Enhanced Apriori Algorithm for Discovering Frequent Patterns with Optimal Number of Scans