| Summary: | 碩士 === 中原大學 === 應用數學研究所 === 100 === Abstract
In fuzzy cluster analysis, the fuzzy c-means (FCM) clustering algorithm is the most well-known and used method. Up to date, there have been many researches on generalized types of FCM. In 2007, Yu and Yang [25] proposed a generalized fuzzy clustering regularization (GFCR) that adding a regularization with membership functions on an objective function. Besides, some authors tried to solve the affects of the clustering results by outliers and noises. Recently, Yu et al. [4] proposed sample-weighted clustering methods that apply the maximum entropy principle to automatically compute these sample weights for clustering to improve their clustering strengths.
In this thesis, we give a sample-weighted version of generalized fuzzy clustering regularization (GFCR), called the sample-weighted fuzzy clustering with regularizations (SW-GFCR). Some numerical examples are considered. These experimental results and comparisons actually demonstrate that the proposed SW-GFCR is more effective and robust than GFCR、SW-KM、SW-FCM and SW-EM.
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