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.
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Lee, K.H., Kim, K.S., Lee, J.H., Park, J.H., Kim, D.G., Kim, D.S. (2007). Development of Enhanced Data Mining System to Approximate Empirical Formula for Ship Design. In: Zhang, Z., Siekmann, J. (eds) Knowledge Science, Engineering and Management. KSEM 2007. Lecture Notes in Computer Science(), vol 4798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76719-0_42
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DOI: https://doi.org/10.1007/978-3-540-76719-0_42
Publisher Name: Springer, Berlin, Heidelberg
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