Development of enhanced data mining system to approximate empirical formula for ship design

Authors:
Kyung Ho Lee;Kyung Su Kim;Jang Hyun Lee;Jong Hoon Park;Dong Geun Kim;Dae Suk Kim
Affiliations:
INHA University, Department of Naval Architect & Ocean Engineering, Nam-gu, Inchon, Korea;INHA University, Department of Naval Architect & Ocean Engineering, Nam-gu, Inchon, Korea;INHA University, Department of Naval Architect & Ocean Engineering, Nam-gu, Inchon, Korea;INHA University, Graduate School, Department of Naval Architect & Ocean Engineering, Nam-gu, Inchon, Korea;INHA University, Graduate School, Department of Naval Architect & Ocean Engineering, Nam-gu, Inchon, Korea;INHA University, Graduate School, Department of Naval Architect & Ocean Engineering, Nam-gu, Inchon, Korea
Venue:
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Year:
2007

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Abstract

Companies must have tools to manage effectively their huge engineering data. So we developed a data mining system based on GP which can be one of the components for the realization of the utilization of engineering data. The System can derive and extract necessary empirical formulas to predict design parameters in ship design. But we don't have enough data to carry out the learning process of genetic programming. When the learning data is not enough, not good result such like overfitting can be obtained. Therefore we have to reduce the number of input parameters or increase the number of learning and training data. In this paper we developed the improved data mining system by Genetic Programming combined with Self Organizing Map (SOM) to solve these problems. By using this system, we can find and reduce the input parameters which do not influence on output less, as a result of this study we can solve these problems.