Abstract
The model of local pattern analysis provides sound solutions to many multi-database mining problems. In this chapter, we will discuss different types of extreme association rules in multiple databases viz., heavy association rule, high-frequency association rule, low-frequency association rule and exceptional association rule. Also, we show how one can apply the model of local pattern analysis more systematically and effectively. For this purpose, we introduce an extended model of local pattern analysis. We apply the extended model to mine heavy association rules in multiple databases. Also, we justify why the extended model works more effectively. We develop an algorithm for synthesizing heavy association rule in multiple databases. Furthermore, we show that the algorithm identifies whether a heavy association rule is high-frequency rule or exceptional rule. We have provided experimental results obtained for both synthetic and real-world datasets and carried out detailed error analysis. Furthermore, we bring a detailed comparative analysis by contrasting the proposed algorithm with some of those reported in the literature. This analysis is completed by taking into consideration the criteria of execution time and average error.