Cluster detection in background noise
Pattern Recognition
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A note on the convergence of the mean shift
Pattern Recognition
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
IEEE Transactions on Knowledge and Data Engineering
Localized feature selection for clustering
Pattern Recognition Letters
Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
Computational Statistics & Data Analysis
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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.