An Algorithm for Mining Conditional Hybrid Dimensional Association Rules using Boolean Matrix

In this paper, we present an algorithm of mining hybrid dimension association rules which satisfies the definite condition on the basis of multidimensional transaction database. Boolean Matrix based approach has been employed to generate frequent itemsets in multidimensional transaction databases. It adopts Boolean relational calculus to discover frequent predicate sets. When using this algorithm first time, it scans the database once and will generate the association rules. Apriori property is used in algorithm to prune the item sets. It is not necessary to scan the database again; it uses Boolean logical operations to generate the association rules. We illustrate the implementation of algorithm with the help of a simple example.