Alternative c-means clustering algorithms

By Kuo-Lung Wu, Miin-Shen Yang. Pattern Recognition, 2002

561 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/cd7b21bf142317b9853ed8d76076996d9161dbb4

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Citations in the corpus

A novel kernelized fuzzy C-means algorithm with application in medical image segmentation (2004, methodology): “In this section, we are only interested in M-estimator and follow the process of proof in [22].”
A cluster validity index for fuzzy clustering (2005, background): “Especially, Wu and Yang (2002) had claimed that an exponential-type distance gives robust property based on the inﬂuence function analysis.”
Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation (2007, background): “Fast generalized fuzzy c-means algorithms FCM_S1 and FCM_S2, as two extensions to FCM_S, have yielded effective segmentation for images [15], but both still have some disadvantages: 1) Although the introduction of local spatial information enhances their insensitiveness to noise to some extent, FCM_”
Improving fuzzy c-means clustering based on feature-weight learning (2004, background): “In (Wu and Yang, 2002), the authors proposed a new robust metric, which is distinguished from the Euclidean distance, to improve the robustness of FCM.”
Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting (2005, methodology): “In order to examine the effectiveness of OPA applied to the prediction, the comparison with another two commonly used algorithms in training RBF neural networks, HCM and OLS [18–22], is performed.”
Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure (2004, methodology): “, (13), are robust to outliers and noise according to Huber’s robust statistics [19], [23].”
Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation (2013, unknown): “Particularly , the clustering algorithms based on the kernel methods have been shown to be robust to the outliers or noises of the dataset [17].”
A robust EM clustering algorithm for Gaussian mixture models (2012, methodology): “The most popular partition methods with cluster prototypes are k -means [6,7], trimmed k -means [8,9], fuzzy c-means (FCM) [10,11], and mean shift [12,13].”
Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance (2004, background): “Pal and Pal, 1991, 1992) and in cluster analysis (cf. Wu and Yang, 2002; Yang and Wu, 2004).”