| Summary: | 碩士 === 臺南師範學院 === 教師在職進修資訊碩士學位班 === 90 === Partition clustering methods are given a set of initial centers, it uses an iterative realocation technique that attempts to improve the partitioning by moving objects from one group to another and to achieve global optimality. There is a phenomenon in using partition clustering method to deal with data, the member in someone cluster will no change before the whole iterative stop. This time if we can skin to compute the partition that will save much time. Therefore, in this thesis, we modify the traditional partition clustering methods by applying the isolation ratio, where the isolation ratio is the distance between two clusters, divided by its own standard deviation. Our method is to use the isolation ratio to find the unmove parts and skip them, which can reduces the number of data to calculate with each center point, so it can save much of this time, but some computational time must be spent to calculate the relation of the distance between each center point. From the simulation results by testing some datasets, our method is faster than traditional partition clustering methods.
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