A New Evolutionary Algorithm for Extracting a Reduced Set of Interesting Association Rules

Data mining techniques involve extracting useful, novel and interesting patterns from large data sets. Traditional association rule mining algorithms generate a huge number of unnecessary rules because of using support and confidence values as a constraint for measuring the quality of generated rules. Recently, several studies defined the process of extracting association rules as a multi-objective problem allowing researchers to optimize different measures that can present in different degrees depending on the data sets used. Applying evolutionary algorithms to noisy data of a large data set, is especially useful for automatic data processing and discovering meaningful and significant association rules. From the beginning of the last decade, multi-objective evolutionary algorithms are gradually becoming more and more useful in data mining research areas. In this paper, we propose a new multi-objective evolutionary algorithm, MBAREA, for mining useful Boolean association rules with low computational cost. To accomplish this our proposed method extends a recent multi-objective evolutionary algorithm based on a decomposition technique to perform evolutionary learning of a fitness value of each rule, while introducing a best population and a class based mutation method to store all the best rules obtained at some point of intermediate generation of a population and improving the diversity of the obtained rules. Moreover, this approach maximizes two objectives such as performance and interestingness for getting rules which are useful, easy to understand and interesting. This proposed algorithm is applied to different real world data sets to demonstrate the effectiveness of the proposed approach and the result is compared with existing evolutionary algorithm based approaches.