隨著科技進步，許多企業開始會為顧客進行分群，進行精準行銷，因此，分群分析在資料探勘中是一個非常重要的技術，近年來也常被用來處理很多類別型資料。然而，沒有任何一個分群演算法可以對所有的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)的新分群演算法。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，將會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、人工蜂群演算法及實數型基因演算法。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)。接下來，將它們對Balance scale、Balloons、Lymphography、Car Evaluation、Soybean (small)、Solar flare、Lenses、Monks、Tic-tac-toe endgame 及Hayes-roth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、模糊c-means演算法、模糊Ｋ-mode演算法及直覺核心模糊c-means演算法。實驗結果顯示PSO-KIFCM演算法在較多的資料集有最好的表現。此外，ABC-KIFCM演算法在進行 App 顧客區隔分析上有較好的表現,進而幫助企業做更精準的行銷。

Recently, many companies use clustering analysis to segment customers, and more and more studies focus on categorical data. However, there is no clustering method which can cluster all different datasets correctly. Therefore, this study intends to propose a model which is hybrid of metaheuristics 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 metaheuristics 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), ABC-based KIFCM (ABC-KIFCM) and real-coded GA-based KIFCM (GA-KIFCM. Their results are compared with those of other clustering algorithms, such as K-means, fuzzy c-means (FCM), intuitionistic fuzzy c-means (IFCM), fuzzy K-mode, and KIFCM algorithms using Balance scale, Balloons, Lymphography, Car Evaluation, Soybean (small), Solar flare, Lenses, Monks, Tic-tac-toe endgame and Hayes-roth datasets. From the experimental results, the proposed PSO-KIFCM algorithm has the best performance than the other clustering algorithms in six benchmark datasets. Furthermore, ABC-KIFCM algorithm can stably obtain the optimal solution in App user data. Thus, ABC-KIFCM algorithm is applied to do the App customer segmentation and help the company conduct audience targeting.

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