Study of select items in different data sources by grouping.

Many large organizations have multiple large databases as they transact from multiple branches. Many important decisions are based on a set of specific items called the select items. Thus, the analysis of select items in multiple databases is an important issue. For the purpose of studying select items in multiple databases, one might need true global patterns of select items. Thus, we propose a model of mining global patterns of select items from multiple databases. A measure of overall association between two items in a database is proposed. We have extended the proposed measure for a database whose transactions contain items along with the quantities purchased. We have designed an algorithm based on proposed measure for the purpose of grouping the frequent items in multiple databases. In addition, we have studied properties of different measures proposed in this paper. Experimental results are presented for both real and synthetic databases. [ABSTRACT FROM AUTHOR]