Authors : K. Shyamala and S.P. Rajagopalan
Abstract: Since the introduction of association rules, many algorithms have been developed to perform the computationally very intensive task of association rule mining. During recent years, there has been the tendency in research to concentrate on developing algorithms for specialized tasks, for example, mining optimized rules or incrementally updating rule sets. The classic problem of association rules deals with efficient generation of association rules with respect to minimum support and minimum confidence. But the performance problem concerning this task is still not adequately solved. In this study, a theoretical model of algorithm is presented which generates set of essential rules directly without generating the entire set of association rules. A set of pruning rules are formed and they are applied in the design of the algorithm for generating the essential set of rules. The set of essential rules are the set of predictive class association rules. The efficiency of the proposed algorithm is analyzed theoretically. The application of this algorithm avoids redundant computation and also the time required for generating the essential set of rules is subsequently reduced.
K. Shyamala and S.P. Rajagopalan , 2007. Mining Essential and Interesting Rules for Efficient Prediction . Asian Journal of Information Technology, 6: 1192-1195.