Robust local feature weighting hard c-means clustering algorithm

• Based on a non-Euclidean metric, a robust local feature weighting hard c-means (RLWHCM) clustering algorithm is presented.
• The robustness of RLWHCM is analyzed by using the location M-estimate in robust statistical theory.
• The convergence proof of RLWHCM is given.

In view of local feature weighting hard c-means (LWHCM) clustering algorithm sensitive to noise, based on a non-Euclidean metric, a robust local feature weighting hard c-means (RLWHCM) clustering algorithm is presented. RLWHCM is a natural, effective extension of LWHCM. The robustness of RLWHCM is analyzed by using the location M-estimate in robust statistical theory. The convergence proof of RLWHCM is given. Experimental results on synthetic and real-world data sets demonstrate the effectiveness of the proposed algorithm.