Abstract. The big challenge in discovering association rules is to find the largest frequent itemsets. Sequential algorithms do not have
analytical ability, especially in terms of run-time performance, for such very large databases. Therefore, we must rely on high
performance parallel and distributed computing. We present a new parallel algorithm for frequent itemset mining, called
HoriVertical algorithm. The algorithm passes the database only one time and starts a new stage with the finished itemsets while
some other itemsets in the same stage have not been finished yet. Also, the new algorithm is based on partitioning the database
vertically and horizontally. We present the result on the performance of our algorithm on various databases, and compare
it against well-known algorithms.