A New Parallel Partition Prime Multiple Algorithm for Data Mining

One of the important problems in data mining is discovering association rules from databases. Each transaction contains a set of items. Discovering the frequent itemsets require a lot of computation power, memory and input/output values, which can only be provided by parallel computer. In this paper, we proposed a new Parallel Partition Prime Multiple Algorithm for association rule mining. Proposed algorithm addresses the shortcoming of previously proposed Parallel Buddy Prima Algorithm. The proposed algorithm divides transaction database equally according to their assignment of variable for each processor. The decision of assignment of next transaction to the processor depends on the value of count variable of itemset per transaction. It reduces the time and data complexity.