近年來隨著資訊科技的發展，越來越多領域開始藉由資料探勘的方法來分析資料，其中的分群分析已被廣泛應用，如顧客區隔、圖像分割等，希望能將資料轉化成有利用價值的資訊。然而，沒有任何一個分群演算法能適用於所有資料集，以及有效因應具有異常值的資料集。此外，在現實世界的資料集中，每個特徵給予的影響度必定不同，但很少分群演算法解決此問題。因此，本研究提出了直覺模糊加權c排序均值演算法(intuitionistic fuzzy weighted c-ordered-means, IFWCOM),其結合了直覺模糊集合(intuitionistic fuzzy sets, IFSs), 特徵權重(feature-weighted)及模糊c排序均值演算法(fuzzy c-ordered-means, FCOM)來處理上述所提之問題。此外，本研究進一步將其結合實數型基因演算法(real-coded genetic algorithm)，以提升分群的結果，其被命名為基因演算法為基礎之直覺模糊加權c排序均值演算法(GA-IFWCOM)。接著，將這些方法對Iris、Wine、wbc、wbcd、Glass、Seeds、Pima Indians、Vowel、Flame及Breast Tissue十個資料集進行分群，並將其結果與其他分群演算法進行比較，包括K均值分群演算法、模糊c均值演算法、直覺模糊c均值演算法、特徵權重模糊c均值演算法及模糊c排序均值演算法。實驗結果顯示，基因演算法為基礎之直覺模糊加權c排序均值演算法(GA-IFWCOM)在大多數的資料集中，能獲得較其他分群演算法更加傑出的表現。

Recently, with the advance of the information technology, many fields use data mining to transform data into useful information. Clustering analysis is a very important technology in data mining which has been widely applied in many fields, such as customer segmentation, image segmentation and so on. However, there is no clustering methods which are suitable for all kinds of datasets. For instance, the dataset has outliers. In the real world dataset, the impact of every feature is different. However, seldom researches tried to solve this problem. Thus, this study proposes an intuitionistic fuzzy weighted c-ordered-means (IFWCOM) algorithm which combines the intuitionistic fuzzy sets (IFSs), feature-weighted, and fuzzy c-ordered-means (FCOM) together. Moreover, this study uses genetic algorithm to improve the clustering performance of IFWCOM algorithm. This method is called real-coded genetic algorithm-based IFWCOM (GA-IFWCOM) algorithm. Then, ten datasets including Iris, Wine, wbc, wbcd, Glass, Seeds, Pima Indians, Vowel, Flame and Breast Tissue are employed to verify the proposed algorithm’s clustering performance with the other clustering algorithms which are K-means, fuzzy c-means (FCM), intuitionistic fuzzy c-means (IFCM), improved feature-weighted fuzzy c-means (IFWFCM), and fuzzy c-ordered-means (FCOM) algorithms. According to the experimental results, the GA-IFWCOM algorithm has the better clustering accuracy than the other clustering algorithms for most of datasets.

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