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24 August 1998 Overview of data clustering approaches for simulation scientists
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The increasing complexity of systems requires the use of simulation to help guide the engineers and decision makers by predicting behavior of these systems under future conditions. There are three elements of these analyses: (1) characterizing the conditions - how many parameters are needed at what resolution over what span of time, (2) characterizing the source system - what is it we want to know, how well do we need to define the system's state and (3) characterizing the outputs - what variables tell us the most. The simulation process itself must be cost effective. The total simulation experiment must be done in a timely manner on available computers. We must try to minimize the number of parameters that characterize the environment, a minimum number of components in the model, a minimum span of simulation clock time, and a minimum number of output variables. Where we are using a body of input data to characterize the system, data clustering can help with this reduction process.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas C. Fall "Overview of data clustering approaches for simulation scientists", Proc. SPIE 3369, Enabling Technology for Simulation Science II, (24 August 1998);

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