A study on multi-kernel intuitionistic fuzzy C-means clustering with multiple attributes

Abstract Fuzzy C-means (FCM) clustering has been widely applied in various data-driven applications. While traditional FCM clustering algorithms handle uncertainty with type-2 Fuzzy Sets (T2 FSs), the recently-proposed intuitionistic fuzzy sets (IFSs) have shown advantages for describing vague and uncertain data by taking both membership degree and non-membership degree into account. However, intuitionistic fuzzy C-means (IFCM) algorithms generally do not take the importance of individual attributes and the structure of the data into account, when multi-modal and imbalanced features are involved in an application. Therefore, in this paper, we propose to address this issue of IFCM with multi-kernel mapping. First, different types of features are grouped. Second, a composite kernel is constructed to map each attribute group into an individual kernel space and to linearly combine these kernels with optimimal weights. Comprehensive experiments have been conducted on a wide range of datasets, such as machine learning repository (UCI) dataset, fabric dataset, hyperspectral imaging classification and MRI brain images segmentation to demonstrate the superior performance of the proposed clustering algorithm in comparison with the state-of-the-art in the field.