Feature-reduction fuzzy co-clustering approach for hyper-spectral image analysis

Fuzzy co-clustering algorithms are the effective techniques for multi-dimensional clustering in which all features are considered of equal importance (relevance). In fact, the features’ importance could be different, even several of them could be considered redundant. The removal of the redundant features has formed the idea of feature-reduction in problems of the big data processing. In this paper, we propose a new unsupervised learning scheme by incorporating the feature-weighted entropy into the objective function of fuzzy co-clustering, called the Feature-Reduction Fuzzy Co-Clustering Algorithm (FRFCoC). First, a new objective function is formed on the basis of the original fuzzy co-clustering objective function which adds parameters representing the entropy weight of the different features. Next, a feature-reduction and clustering automatic schema are adjusted based on FCoC's original learning schema which calculates new parameters and conditions to eliminate irrelevant feature components. FRFCoC algorithm can be mathematically shown to converge after a finite number of iterations. The experiment results were conducted on some many-features data sets and hyperspectral images that have demonstrated the outstanding performance of FRFCoC algorithm compared with some previously proposed algorithms. © 2020 Elsevier B.V.