Generalized fuzzy c-means clustering strategies using Lp norm distances

By Richard J. Hathaway, James C. Bezdek, Yingkang Hu. IEEE transactions on fuzzy systems, 2000

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d6d7e7cc82cca53f9c09ed08c89059e1cde4fb2a

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Citations in the corpus

Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure (2004, methodology): “To deal with the first problem, some researchers adopted so-called robust distance measures, such as Lp norms (0<p≤1)[28-30], to replace the L2 norm in the FCM objective function reducing the effect of outliers on clustering results, and while many other algorithms have also been proposed to deal wi”
Resilient and sustainable supply chain design: sustainability analysis under disruption risks (2018, background): “A larger value for m leads to smaller membership values, wij, and therefore, fuzzier clusters (Bezdek, Ehrlich, and Full 1984; Hathaway, Bezdek, and Hu 2000).”
A Robust Fuzzy Local Information C-Means Clustering Algorithm (2010, background): “1) Although the introduction of local spatial information enhances their insensitiveness to noise to some extent, they still lack enough robustness [ 18 ]‐[20] to noise and outliers, especially in absence of prior knowledge of the noise.”
Survey of clustering algorithms (2005, background): “rectly on the data objects or indirectly on the dissimilarity measures [130].”
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_”
Clustering: A neural network approach (2010, neutral): “Other robust extensions to the FCM includes the Lp-norm clustering (0 p 1) (Hathaway & Bezdek, 2000) and the L1-normclustering (Kersten, 1999).”
Image segmentation by generalized hierarchical fuzzy C-means algorithm (2015, background): “A more general study using lp norm distances can be found in [10].”
A review of clustering techniques and developments (2017, background): “Changing the proximity distance can improve the performance of FCM in relation to outliers [44].”