Multi-attribute based fuzzy C-means in approximated feature space

Recently, clustering illustrates its importance in knowledge discovery. However, most of the considered algorithms are efficient only on those linear separable datasets. Although the kernel based methods perform better with non-linear separable ones, at the same time, they are suffered from the missing of priority knowledge of a suitable kernel. Two latest methods which approximate date to infinity dimensional kernel feature spaces are considered in this paper. Furthermore, new attribute multi-kernel weighted fuzzy c-means are proposed by combining the randomly projected features. From the experiments shown in the simulation section, we conclude that we develop an improved version of attribute multi-kernel weighted fuzzy c-means, which achieves better accuracy.