本論文針對區間型態的資料的分群及預測進行分析及探討。區間型態資料和單點型態資料，從表面數字來說僅有資料個數的差別而已(單個數值及兩個數值)，然而，區間型態資料並非只有表面數字上的意涵，其區間數值也具有數字代表的物理意義。因此本篇論文提出一種考量區間型態資料物理意義的改良式模糊c均值演算法(Fuzzy c-means, FCM)，將range的值視為區間資料中心點的可靠程度參考，於目標函式乘上一個與range成反比的exponential函數，如此便能降低range較大(較不可靠)的資料對分群結果的影響，在後續的實驗結果中也能看到同時考慮數值及物理意義的改良式分群法對於分群結果的改善。另一方面，分群後各群的質心點能作為其預測模型的區域特徵點，且區間型態資料的物理意義也能於徑向基底函數網路預測模型中運用，以提升預測模型的效能。

In this thesis, interval-valued data clustering methods and forecasting models are studied and discussed. From a very simple viewpoint, the difference between interval-valued data and single-point data is just the number of data attributes for the dataset. However, those attributes (lower/upper bounds or centre/range) have physical meanings. In this study, an adaptive fuzzy c-means clustering method is proposed to include physical meanings of interval values. The range is regarded as the confidence of the centre data and an exponential function which is inverse proportion to the range is added into the objective function to account for this confidence idea. Then the data with less confidence obtain less influence in the training process. It can be found from our experiments that with both numerical and physical meanings in our approach, the clustering performance is improved and this improvement can also benefit the following data analysis process. With the cluster centroids obtained from the proposed adaptive fuzzy c-means clustering method in radial basis function networks (RBFN), the forecasting model can be constructed. With the same idea of including physical meaning of interval value in the training process, the obtained model can have better predictions as shown in our experiments.

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