The paper deals with a special class of cluster analysis methods where a membership degree is calculated for each object and each cluster. These methods are investigated under the name fuzzy cluster analysis. We present some emerging topics in this area, such as relation fuzzy clustering, soft clusters ensembles, similarity of fuzzy clusters, visualization of clustering results, simultaneous clustering and feature discrimination, and techniques for cluster number determination. Some tasks are illustrated by clustering of binary variables
AbstractThis paper presents a visualization of a result of fuzzy clustering. The feature of fuzzy cl...
Cluster analysis is highly advantageous as it provides “relatively distinct” (or heterogeneous) clu...
The cluster analysis of fuzzy clustering according to the fuzzy c-means algorithm has been described...
The application of fuzzy cluster analysis to larger data sets can cause runtime and memory overflow ...
Provides a timely and important introduction to fuzzy cluster analysis, its methods and areas of app...
One of the most interesting and promising approaches to the analysis of multivariate phenomena and p...
• Cluster A number of similar individuals that occur together as a two or more consecutive features ...
Abstract — In recent years, data mining is widely preferred area by researcher for discovering new k...
Abstract. In this paper, we study and improve the fuzzy clustering index and clustering algorithm pr...
The fuzzy clustering algorithm is to classify the data or indicators with a greater degree of simila...
Fuzzy logic is an organized and mathematical method of handling inherently imprecise concepts throug...
The paper focuses on the development of selected approaches in cluster analysis. There are recently ...
Clustering is one of the most used tools in data analysis. In the last decades, due to the increasin...
Cluster analysis is highly advantageous as it provides “relatively distinct” (or heterogeneous) clu...
One of the shortcomings of the existing clustering methods is their problems dealing with different ...
AbstractThis paper presents a visualization of a result of fuzzy clustering. The feature of fuzzy cl...
Cluster analysis is highly advantageous as it provides “relatively distinct” (or heterogeneous) clu...
The cluster analysis of fuzzy clustering according to the fuzzy c-means algorithm has been described...
The application of fuzzy cluster analysis to larger data sets can cause runtime and memory overflow ...
Provides a timely and important introduction to fuzzy cluster analysis, its methods and areas of app...
One of the most interesting and promising approaches to the analysis of multivariate phenomena and p...
• Cluster A number of similar individuals that occur together as a two or more consecutive features ...
Abstract — In recent years, data mining is widely preferred area by researcher for discovering new k...
Abstract. In this paper, we study and improve the fuzzy clustering index and clustering algorithm pr...
The fuzzy clustering algorithm is to classify the data or indicators with a greater degree of simila...
Fuzzy logic is an organized and mathematical method of handling inherently imprecise concepts throug...
The paper focuses on the development of selected approaches in cluster analysis. There are recently ...
Clustering is one of the most used tools in data analysis. In the last decades, due to the increasin...
Cluster analysis is highly advantageous as it provides “relatively distinct” (or heterogeneous) clu...
One of the shortcomings of the existing clustering methods is their problems dealing with different ...
AbstractThis paper presents a visualization of a result of fuzzy clustering. The feature of fuzzy cl...
Cluster analysis is highly advantageous as it provides “relatively distinct” (or heterogeneous) clu...
The cluster analysis of fuzzy clustering according to the fuzzy c-means algorithm has been described...