分群分析在資料探勘中是一個非常重要的議題，最近幾年許多的分群演算法已被提出。然而，沒有任何一個分群演算法可以對所有不同資料集進行正確的分群。因此，本研究提出萬用演算法結合核心直覺模糊c-means演算法 (kernel intuitionistic fuzzy c-means, KIFCM)的進化分群演算法。核心直覺模糊c-means演算法結合了直覺模糊集合( intuitionistic fuzzy sets, IFSs) 與核心模糊c-means演算法(kernel-based fuzzy c-means, KFCM)。在本研究中，應用三種萬用演算法來改善分群的結果，分別是粒子群演算法、實數型基因演算法及人工蜂群演算法。因此，本研究提出三種分群演算法，包括粒子群演算法為基礎之核心直覺模糊c-means演算法(PSO-KIFCM)、實數型基因演算法為基礎之核心直覺模糊c-means演算法(GA-KIFCM)及人工蜂群演算法為基礎之核心直覺模糊c-means演算法(ABC-KIFCM)。接著，將它們對Iris、Wine、Tae、Flame、Glass及Wbc 資料集的分群結果和其他分群演算法進行比較，分群演算法有K-means分群演算法、模糊c-means演算法、直覺模糊c-means演算法、核心模糊c-means演算法及直覺核心模糊c-means演算法。根據實驗的結果，在分群的準確率上，實數型基因演算法為基礎之核心直覺模糊c-means演算法(GA-KIFCM)比其他分群演算法能獲得更好的結果。

Clustering analysis is an important issue in data mining. Many clustering algorithms have been proposed in recent years. However, there is no clustering method which can process all different datasets correctly. Thus, this study intends to propose evolutionary-based clustering algorithms which are hybrid of meta-heuristic algorithms and kernel intuitionistic fuzzy c-means (KIFCM) algorithm. The KIFCM algorithm combines Atanassov’s intuitionistic fuzzy set (IFSs) with kernel-based fuzzy c-means (KFCM). In this study, three meta-heuristic algorithms are applied to improve the clustering results, which are particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and artificial bee colony (ABC) algorithm. Therefore, there are three clustering algorithms proposed including PSO-based KIFCM (PSO-KIFCM), real-coded GA-based KIFCM (GA-KIFCM), and ABC-based KIFCM (ABC-KIFCM). Their results are compared with those of other clustering algorithms, such as K-means, fuzzy c-means (FCM), intuitionistic fuzzy c-means (IFCM), KFCM, and KIFCM using iris, wine, tae, flame, glass, and wbc datasets. From the experimental results, the proposed GA-KIFMC is able to obtain better solutions than the other clustering algorithms in six benchmark datasets, in terms of accuracy.

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