Abstract

. Many large companies transact from multiple branches. It results in generating multiple databases, since local transactions are stored locally. The number of multi-branch companies as well as the number of branches of a multi-branch company is increasing over time. Thus, it is important to study data mining on multiple databases. Global exceptional patterns describe interesting individuality of few branches. Therefore, it is interesting to identify such patterns. In this paper, we propose type I and type II global exceptional frequent itemsets in multiple databases by extending the notion of global exceptional frequent itemset. Also, we propose the notion of exceptional sources for a type II global exceptional frequent itemset. We propose type I and type II global exceptional association rules in multiple databases by extending the notion of global exceptional association rule. We propose an algorithm for synthesizing type II global exceptional frequent itemsets. Experimental results are presented on both real and synthetic databases. We compare the proposed algorithm with the existing algorithm theoretically as well as experimentally. The experimental results show that the proposed algorithm is effective and promising.