Remote classification of images using the new fuzzy clustering algorithm based on the weight

Fuzzy clustering model is an essential tool to find the proper cluster structure of given data sets in pattern and image classification. In this paper, a new weighted fuzzy C-Means (NW-FCM) algorithm is proposed to improve the performance of NW-FCM models for high-dimensional multiclass pattern recognition problems and its result has been compared with the results of FCM, NFCC, and NW-FCM fuzzy clustering algorithm. The methodology used in NW-FCM is the concept of weighted mean from the nonparametric weighted feature extraction (NWFE) and cluster mean from discriminant analysis feature extraction (DAFE). These two concepts are combined in NW-FCM for unsupervised clustering. The main feature of this algorithm comparing the other three one includes the weight mean for increasing the accuracy. The motivation of this work is amending the NW-FCM algorithm. The results of this paper indicates that the proposed algorithm has higher accuracy comparing with NW-FCM while clustering. Experimental results on both synthetic and real data demonstrate that the proposed clustering algorithm will generate better clustering results than those of FCM and FWCM algorithms, in particular for hyperspectral images