An Improved Distributed Association Rule Algorithm

Abstract

All Distributed association rules mining (DARM) algorithms which bases on Apriori algorithm don't have an efficient message optimization technique, so they exchange numerous messages during the mining process which needs several distributed scan operations to the distributed warehouses or distributed databases to get the support values, also the performance of these DARM algorithms decreased with increasing communication cost especially when increasing the number ofdistributed mining sites as well as the itemsets to be mined become more larger . The aim of this work is to improve association rules in distributed data mining by proposing a new efficient method of distributed association rule mining, which reduce the average size of records transferred, datasets and messages transferred without needto any distributed scan to the distributed data warehouses or distributed databases to retrieve the values of the support values of these datasets. The results obtained from the proposed method prove that the proposed method is better than the existing algorithms by reducing communications costs, centralstorage requirements, enhanceperformance and achieves high degree of scalability compared with the existing algorithms.