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Enrico Seib (2008)

Data-Mining-Methoden in der Simulation

Bachelorthesis, Universität Rostock.

Principles and methods of data mining are a widespread area, i.e. retail dealer use data mining tools to analyze the behavior of customers, computer hardware supplier use data mining to optimize their inventory. There are multiple possibilities of using data mining techniques, even in technical and scientific areas of applications. In regard of manyfold fields of application, there are no less than the number of techniques and methods for Data Mining in existence. Another field to apply Data Mining technique is the domain of simulation. Simulation is the computer-based approach of executing and experimenting of and with models. One aim of this thesis is to analyze data mining tools to see how capable they are solving data mining duties with respect to data calculated by simulation. Different data mining tools are analyzed, commercial tools like SPSS and SPSS Clementine as well as established and freely available tools like WEKA and the R-Project. These tools are analyzed in matters of their data mining functionalities, options to access different data sources, and their complexity of different data mining algorithms. Beyond the analysis of data mining tools with respect to functionality and simulation, environments for modeling and simulation are analyzed with respect to their possibilities of the utilization for data mining. These environments are the commercial tools Arena and AnyLogic and the freely available SeSam-Project. The effect of all processes of analyzing is a ranking of commonly used data mining techniques and concepts. The second part of the thesis occupies with the problem, which data mining method or technique is useful to analyze data provided by a simulation process. It also concerns in which way a method is suitable for the validation of a certain model. In the long run of this thesis the chosen data mining technique is applied to data generated by a simulation process of diffusion and reaction of substances.

Erschienen bei GRIN, http://dx.doi.org/10.3239/9783640145478
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