Developing kernel intuitionistic fuzzy c-means clustering for e-learning customer analysis

This study develops the kernel intuitionistic fuzzy c-means clustering (KIFCM), and applies KIFCM in E-learning customer analysis. KIFCM combines intuitionistic fuzzy sets (IFSs) with kernel fuzzy c-means clustering (KFCM). The KIFCM has advantages of IFSs and KFCM which can effectively handle uncertain data and simultaneously map data to kernel space. The proposed KFCM has better performance than k-mean (KM) and fuzzy c-means (FCM) in numerical example. Furthermore, the study adopts the advanced clustering technology in E-learning customer clustering analysis, and analyses customer data based on clustering results by correlation analysis. The customer analysis result can provide for sales department, and assist to obtain customer´s learning tendency in E-learning platform.