| Summary: | 碩士 === 國立虎尾科技大學 === 資訊管理研究所 === 96 === Data mining usually means the methodologies and tools for the efficient new knowledge discovery from databases. In this study a hybrid meta-evolutionary data mining approach as a classification response model is proposed. Moreover, the proposed approach is based on the grid computing infrastructure for establishing the best attributes set.
As the real world problems are highly nonlinear in nature, they are hard to develop a comprehensive model taking into account all the independent variables using the these statistical approaches. Early many studies of handling the problems used the conventional statistical methods and statistical related techniques including logistic regression and multi-normal regressions. Recently, nonlinear and complex machine learning approaches such as neural networks and support vector machines have been demonstrated to be with more reliable than the conventional statistical approaches. Although the usefulness of using these methods has been reported in literatures, the most obstacles are in the building and using the model in which the classification rules are hard to be realized.
For enhancing the mining efficiency in this study, the proposed mining approach is build which is based on the grid computing infrastructure. The discriminant analysis based on vector distant of median method as the evaluation function of GA which lays stress on find the key attributes set of the data set to establish the best attributes set for constructing a classification response model with highest accuracy. Furthermore, to generate the classification rule, additional approach composing the hybrid GA and binary particle swarm optimization method is applied in the grid computing infrastructure to extract the If-Then rules set model.
We show experimentally that the proposed mining approach based on the grid computing infrastructure can work effectively and efficiently, and the results of the proposed methods are better than those in the literature and/or by using business software. In particular, the proposed approach can be developed as a computer model for prediction or classification problem like expert systems.
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