A novel rule infusion technique for generating simulated binary data to validate data mining methods

Mathematical analysis of existing data mining methods is not straightforward and in many cases it is not possible. Therefore, simulated data plays a central role in validation of data mining results in a given situation, i.e., noise, missing value and multicollinearity levels. This paper proposes a longitudinal binary data simulation focusing on presentation of the major challenge of infusing user-defined rules. Results of applying Apriori, PRAT, Prism, and JRip rule extraction methods on these simulated data in several missing value levels are presented in this paper. This simulation proved to be essential in verifying data mining results that we have generated on Medical Epidemiological and Social Aspects of Aging (MESA) data set.