Add to ORKGThis paper proposes a fuzzy clustering technique that captures local non-linear structures of data sets by generalizing Fuzzy c-Varieties (FCV) proposed by Bezdek et al.. While FCV is regarded as the simultaneous application of fuzzy clustering and Principal Component Analysis (PCA), the proposed method is a hybrid technique of fuzzy clustering and general-ized PCA that is useful to non-linear dimension reduction. The clustering result is closely related to Shell clustering that partitions data sets into several shell-shape clusters by using quadric shells as the prototypes
A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints impo...
This article presents a New Neutrosophic C-Means (NNCMs) method for clustering. It uses the neutroso...
Abstruct- Traditionally, prototype-based fuzzy clustering al-gorithms such as the Fuzzy C Means (FCM...
Fuzzy c-varieties (FCV) is one of the clustering algorithms in which the prototypes are multi-dimens...
This paper proposes a new robust approach to nonlinear clustering based on the Principal Component A...
FCM-type fuzzy clustering approaches are closely related to Gaussian Mixture Models (GMMs) and the o...
[[abstract]]The popular fuzzy c-means algorithm (FCM) is an objective function based clustering meth...
The fuzzy c-means (FCM) clustering algorithm has long been used to cluster numerical data. Recently ...
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, p...
Nowadays, Image segmentation is the area in which most of the research is carried out. It is conside...
Abstract — Clustering is a collection of objects which are similar between them and dissimilar to th...
Provides a timely and important introduction to fuzzy cluster analysis, its methods and areas of app...
This master thesis deals with cluster analysis, more specifically with clustering methods that use f...
[[abstract]]Some of the well-known fuzzy clustering algorithms are based on Euclidean distance funct...
The fuzziness index m has important influence on the clustering result of fuzzy clustering algorithm...
A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints impo...
This article presents a New Neutrosophic C-Means (NNCMs) method for clustering. It uses the neutroso...
Abstruct- Traditionally, prototype-based fuzzy clustering al-gorithms such as the Fuzzy C Means (FCM...
Fuzzy c-varieties (FCV) is one of the clustering algorithms in which the prototypes are multi-dimens...
This paper proposes a new robust approach to nonlinear clustering based on the Principal Component A...
FCM-type fuzzy clustering approaches are closely related to Gaussian Mixture Models (GMMs) and the o...
[[abstract]]The popular fuzzy c-means algorithm (FCM) is an objective function based clustering meth...
The fuzzy c-means (FCM) clustering algorithm has long been used to cluster numerical data. Recently ...
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, p...
Nowadays, Image segmentation is the area in which most of the research is carried out. It is conside...
Abstract — Clustering is a collection of objects which are similar between them and dissimilar to th...
Provides a timely and important introduction to fuzzy cluster analysis, its methods and areas of app...
This master thesis deals with cluster analysis, more specifically with clustering methods that use f...
[[abstract]]Some of the well-known fuzzy clustering algorithms are based on Euclidean distance funct...
The fuzziness index m has important influence on the clustering result of fuzzy clustering algorithm...
A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints impo...
This article presents a New Neutrosophic C-Means (NNCMs) method for clustering. It uses the neutroso...
Abstruct- Traditionally, prototype-based fuzzy clustering al-gorithms such as the Fuzzy C Means (FCM...