| Summary: | 碩士 === 朝陽科技大學 === 資訊工程系 === 106 === In recent years, the rapid development of computer technology and information industry has led to a significant increase in the amount of data. However, regarding these large and messy multidimensional data sets, we cannot quickly and effectively find the information that we need. Therefore, we have to use the data mining techniques to concentrate on extracting the information that we need from the data. In this thesis, we will introduce a relatively new data mining software, Rapidminer. We compare the Rapidminer with other data mining software via comparative analysis of a functional operating procedures. Through the application of four case studies including linear regression, neural networks, decision trees, and support vector machines to illustrate the operations of Rapidminer. There are two reasons to use Rapidminer in this thesis. The first one is that it has a very convenient graphical interface. The second one is that user does not need to learn other programming syntax, just need to select components and setting parameters. The display of analysis results is also diversification, which allowing users to choose the functional map to view the results.
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