Many approaches for identifying potentially interesting items exploiting commonly used techniques of multidimensional data analysis. There is a great need for designing association-rule mining algorithms that will be scalable not only with the number of records (number of rows) in a cluster but also among domain's size (number of dimensions) in a cluster to focus on the domains. Where the items belong to domain is correlated with each other in a way that the domain is clustered into classes with a maximum intra-class similarity and a minimum inter-class similarity. This property can help to significantly used to prune the search space to perform efficient association-rule mining. For finding the hidden correlation in the obtained clusters effectively without losing the important relationship in the large database clustering techniques can be followed by association rule mining to provide better evaluated clusters.