Enhanced Robust Fuzzy K-Means Clustering joint [formula omitted]-norm constraint

Abstract

Clustering is an unsupervised classical data processing technique, in which Fuzzy K-Means is extensively researched in practical application owing to its efficiency. However, common outliers in real-world always lead to clustering degradation. In this paper, we design an Enhanced Robust Fuzzy K-Means clustering method (ERFKM) joint ℓ0-norm sparse nearest neighbor constraint as a countermeasure to above challenge. On one hand, we adaptively weigh different samples by binary vector to measure each contribution for robustness, among which the outliers can be effectively recognized to eliminate their impact on clustering. That is, the weighted term is able to steadily correct the cumulative drift deviation of cluster centroid via ignoring extremely large sample-centroid residuals from outliers. On the other hand, the fuzzy regularizer with ℓ0-norm constraint aims to further enhance the segmentation ability of indicated normal samples through automatic cluster neighbor assignment. Finally, an optimization algorithm for our model is devised to simultaneously update the binary weight of each sample and sparse membership matrix for achieving robust clustering. Comprehensive experiments demonstrate the advantages and effectiveness of the proposed method.