GMKIT2-FCM: A Genetic-based improved Multiple Kernel Interval Type-2 FUzzy C-means clustering
Dzung Dinh Nguyen, Long Thanh Ngo, Long The Pham · 2013
This paper deals with a Genetic Multiple Kernel Interval Type 2 Fuzzy C-means clustering (GMKIT2-FCM), which automatically find the optimal number of clusters and determine the coefficients of the multiple kernel. The proposed GMKIT2-FCM algorithm provides us a new flexible vehicle to fuse different data information in the classification problems. That is, different information represented by different kernels is combined in the kernel space to produce a new kernel. The proposed algorithm contains two main stages. The first, a heuristic method based on Genetic algorithm (GA) and the average multiple kernel interval type 2 fuzzy c-means clustering (MKIT2-FCM) is adopted to automatically determine the optimal number of clusters and the initial the centroids. Then the results of the first stage are used in combination with GA and MKIT2-FCM to adjust the coefficients of multiple kernel to achieve better results. The experiments are done based on well-known datasets with the statistics show that the algorithm generates good quality of clustering problems.