DOI: 10.4018/IJDWM.2018010104 International Journal of Data Warehousing and Mining Volume 14 • Issue 1 • January-March 2018 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 60 Optimization of Mean and Standard Deviation of Multiple Responses Using Patient Rule Induction Method Jin-Kyung Yang, Hanyang University, Seoul, South Korea Dong-Hee Lee, Hanyang University, Seoul, South Korea ABSTRACT In product and process optimization, it is common to have multiple responses to be optimized. This is called multi-response optimization (MRO). When optimizing multiple responses, it is important to consider variability as well as mean of the multiple responses. The authors call this problem as extended MRO (EMRO) where both of mean and variability of the multiple responses are optimized. In this article, they propose a data mining approach to EMRO. In these days, analyzing a large volume of operational data is getting attention due to the development of data processing techniques. Traditional MRO methods takes a model-based approach. However, this approach has limitations when dealing with a large volume of operational data. The authors propose a particular data mining method by modifying patient rule induction method for EMRO. The proposed method obtains an optimal setting of the input variables directly from the operational data where mean and standard deviation of multiple responses are optimized. The authors explain a detailed procedure of the proposed method with case examples. KeyWORDS Data Mining, Design of Experiments, Desirability Function, Multi-Response Optimization, Operational Data, Patient Rule Induction Method, Process Optimization, Response Surface Methodology 1. INTRODUCTION Response surface methodology (RSM) consists of a group of techniques used in empirical study between a response and a number of input variables. The researcher attempts to find the optimal setting for the input variables that either maximizes or minimizes the response (Myers et al., 2009). RSM assumes that variance of the response is constant and focuses on optimizing the mean of the response. However, the constant variance assumption might not be valid in practice. In such cases, not only the mean response, but also the standard deviation of the response should be considered in determining the optimum conditions for the input variables. Dual-response optimization (DRO) attempts to optimize both the mean and standard deviation of the response. The conventional approach to DRO requires building statistical models for mean and