General C-Means Clustering Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust fuzzy clustering neural network based on ε-insensitive loss function
Applied Soft Computing
A robust deterministic annealing algorithm for data clustering
Data & Knowledge Engineering
Computer Vision and Image Understanding
RK-Means Clustering: K-Means with Reliability
IEICE - Transactions on Information and Systems
TS-fuzzy system-based support vector regression
Fuzzy Sets and Systems
K-means clustering versus validation measures: a data-distribution perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Enhanced neural gas network for prototype-based clustering
Pattern Recognition
A maximum profit coverage algorithm with application to small molecules cluster identification
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A comparative study of efficient initialization methods for the k-means clustering algorithm
Expert Systems with Applications: An International Journal
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In many applications of C-means clustering, the given data set often contains noisy points. These noisy points will affect the resulting clusters, especially if they are far away from the data points. In this paper, we develop a pruning approach for robust C-means clustering. This approach identifies and prunes the outliers based on the sizes and shapes of the clusters so that the resulting clusters are least affected by the outliers. The pruning approach is general, and it can improve the robustness of many existing C-means clustering methods. In particular, we apply the pruning approach to improve the robustness of hard C-means clustering, fuzzy C-means clustering, and deterministic-annealing C-means clustering. As a result, we obtain three clustering algorithms that are the robust versions of the existing ones. In addition, we integrate the pruning approach with the fuzzy approach and the possibilistic approach to design two new algorithms for robust C-means clustering. The numerical results demonstrate that the pruning approach can achieve good robustness.