Fuzzy c-means (FCM) is a useful clustering technique. Modifications of FCM using L/sub 1/ norm distances increase robustness to outliers.
In this note, we examine FCM-based clustering using general norm distances, where the norm of the -dimensional real vector is defined as . In Sec- tion II, we ...
Fuzzy c-means (FCM) is a useful clustering technique. Modifications of FCM using L<sub>1</sub> norm distances increase robustness to outliers.
Fuzzy c-means (FCM) is a useful clustering technique. Recent modifications of FCM using L1 norm distances increase robustness to outliers.
Abstract. Fuzzy c-means (FCM) is a useful clustering technique. Recent modifications of FCM using L1 norm distances increase robustness to outliers.
Generalized fuzzy c-means clustering strategies using Lp norm distances. R. Hathaway, J. Bezdek, Yingkang Hu. 2000, IEEE transactions on fuzzy systems. S2 logo ...
This paper presents mathematical description of different distance metrics which can be acquired with different clustering algorithm and comparing their ...
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Generalized Fuzzy C-Means Clustering Strategies Using Lp Norm Distances. By: Bezdek, James C · Hathaway, Richard J. Material type: ArticleDescription ...
The paper presents the L 1 version of the well-known fuzzy clustering method, namely fuzzy ISODATA, proposed by Bezdek and Dunn.
Algorithms for Fuzzy Clustering - Methods in c-Means Clustering with Applications ... Generalized fuzzy c-means clustering strategies using Lp norm distances · R ...