由於資訊和科技的進步，數據的收集也越來越容易。分群是一種用於挖掘數據結構的重要技術且應用於許多領域，例如顧客區隔，圖像識別，社會科學等。然而，在實際應用中，有很多噪音或是異常值在資料集中，這會影響分群技術的表現，此外，分群結果容易受到初始重心和參數的影響，因此，為了改善離群值對於分群結果的影響，本研究結合可能性模糊c均值演算法(possibilistic fuzzy c-means, PFCM)和模糊c排序均值演算法 (fuzzy c-ordered means, FCOM)的優點提出模糊可能性c排序均值演算法 (fuzzy possibilistic c-ordered means, FPCOM)。再者，為了解決參數和初始重心的問題，本研究使用正餘弦演算法結合FPCOM去改善分群結果。所提出的演算法為SCA-FPCOM。有10個從UCI機器庫收集的數據集會被使用於驗證所提出的演算法。本研究所使用的兩個評比指標評估分群表現，包括Adjusted Rand Index (ARI)及Silhouette Coefficient。根據實驗結果，SCA-FPCOM演算法比起其他演算法能夠獲得更佳的結果。此外，所以提出的算法也應用於現實世界中百貨商城的客戶區隔的問題，結果顯示有不錯的表現。

Due to advances in information technology, data collection is becoming much easier. Clustering is an important technique for exploring data structures and is used in many fields, such as customer segmentation, image recognition, social science and so on. However, in real–world applications, there are a lot of noises or outliers which will influence the clustering performance in the dataset. Besides, the clustering results are susceptible to the initial centroids and algorithm parameters. Therefore, in order to overcome the influence of outliers on clustering results, this study combines the advantages of probability c-means (PFCM) and fuzzy c-ordered means (FCOM) to propose a fuzzy possibilistic c-ordered means (FPCOM) algorithm. In order to solve the problem of parameters and initial centroids determination, this study employs a sine cosine algorithm (SCA) combined with FPCOM to improve the clustering results. The proposed algorithm is named SCA-FPCOM algorithm. Ten benchmark datasets collected from the UCI machine repository were used to validate the proposed algorithm in terms of adjusted rand index (ARI) and the Silhouette coefficient. According to the experimental results, the SCA-FPCOM algorithm can obtain better results than other algorithms. In addition, the proposed algorithms were also applied to a real-world problem, mall customer segmentation. The computational result is also very promising.

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