Apriori Association Rule Mining (ARM) is a popular data mining technique for deriving association rules from frequent itemsets, and it has a long history. Despite of its popularity, its performance suffers from a bottleneck in scalability. Many attempts were made in the past, including changing the frequent item database structure to sophisticated parallel execution. In this paper an alternative strategy is proposed which centred on segmenting the database in lieu of using the full database. The segmentation is by ensemble method which sifts and selects the most effective clustering algorithm; the resultant segmented data cluster is subsequently used for ARM. Using only a fraction of data transactions which supposedly has a high concentration of expressive data, ARM produces higher quality association rules at shorter time. The proposed ARM model is tested using three cases - bank, homicide and lung cancer. The results confirm theusefulness of this new model - higher quality rules are gained.