Algorithms for automatic selection of seed points for clustering are described using the terms 'index of fuzziness', 'entropy', and 'π-ness' of a fuzy set. Two membership functions R in have been defined such that the fuzzy measures posses maximum values when the crossover points/central points of the membership functions correspond to the points around which the data has a tendency to cluster. The effectiveness of the algorithm is demonstrated on a set of speech data
In this paper, a new level-based (hierarchical) approach to the fuzzy clustering problem for spatial...
The terms index of fuzziness, entropy, and ¿-ness, which give measures of fuzziness in a set, are u...
The most common fuzzy clustering algorithms are based on the minimization of an objective function t...
Abstract: A three-stage dynamic fuzzy clustering algorithm consisting of initial partitioning, a seq...
The application of fuzzy cluster analysis to larger data sets can cause runtime and memory overflow ...
A hard partition clustering algorithm assigns equally distant points to one of the clusters, where e...
Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number...
Abstract- The well-known generalisation of hard C-means (HCM) clustering is fuzzy C-means (FCM) clus...
textabstractFuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has...
Several clustering algorithms include one or more parameters to be fixed before its application. Thi...
Fuzzy logic is an organized and mathematical method of handling inherently imprecise concepts throug...
Clustering is an important research area that has practical applications in many elds. Fuzzy cluster...
The Fuzzy k-Means (FkM) algorithm is a tool for clustering n objects into k homogeneous groups. FkM ...
Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applie...
Two new algorithms for fuzzy clustering are presented. Convergence of the proposed algorithms is pro...
In this paper, a new level-based (hierarchical) approach to the fuzzy clustering problem for spatial...
The terms index of fuzziness, entropy, and ¿-ness, which give measures of fuzziness in a set, are u...
The most common fuzzy clustering algorithms are based on the minimization of an objective function t...
Abstract: A three-stage dynamic fuzzy clustering algorithm consisting of initial partitioning, a seq...
The application of fuzzy cluster analysis to larger data sets can cause runtime and memory overflow ...
A hard partition clustering algorithm assigns equally distant points to one of the clusters, where e...
Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number...
Abstract- The well-known generalisation of hard C-means (HCM) clustering is fuzzy C-means (FCM) clus...
textabstractFuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has...
Several clustering algorithms include one or more parameters to be fixed before its application. Thi...
Fuzzy logic is an organized and mathematical method of handling inherently imprecise concepts throug...
Clustering is an important research area that has practical applications in many elds. Fuzzy cluster...
The Fuzzy k-Means (FkM) algorithm is a tool for clustering n objects into k homogeneous groups. FkM ...
Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applie...
Two new algorithms for fuzzy clustering are presented. Convergence of the proposed algorithms is pro...
In this paper, a new level-based (hierarchical) approach to the fuzzy clustering problem for spatial...
The terms index of fuzziness, entropy, and ¿-ness, which give measures of fuzziness in a set, are u...
The most common fuzzy clustering algorithms are based on the minimization of an objective function t...
DOI
Add to ORKG