Add to ORKGSince clustering is an unsupervised method and there is no a-priori indication for the actual number of clusters presented in a data set, there is a need of some kind of clustering results validation. In this paper, we propose a new cluster validity index for the fuzzy clustering algorithms. This validation includes two levels. The first during the clustering process for identifying the worst cluster to delete it. The second includes the validity function for evaluating the set of the resulting partitions
This chapter presents a unified framework to generalize a number of fuzzy clustering algorithms to h...
Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number...
In a clustering problem, it would be better to use fuzzy clustering if there was an uncertainty in d...
Fuzzy clustering is useful to mine complex and multi-dimensional datasets, where the members have pa...
Cluster validation is a major issue in cluster analysis. Many existing validity indices do not perfo...
Abstract An improved cluster validity index for fuzzy clustering that is able to overcome three intr...
AbstractIn this paper, we define a validity measure for fuzzy criterion clustering which is a novel ...
Cluster analysis is an important tool in the exploration of large collections of data, revealing pat...
Clustering can be defined as the process of grouping physical or abstract objects into classes of si...
Because traditional fuzzy clustering validity indices need to specify the number of clusters and are...
Abstract: Finding the optimal cluster number and validating the partition results of a data set are ...
Abstract—Identification of correct number of clusters and the corresponding partitioning are two imp...
Cluster analysis is a multivariate statistical classification method, implying different methods and...
To measure the fuzziness of fuzzy sets, this paper introduces a distance-based and a fuzzy entropyba...
Abstract:- Clustering is a process of discovering groups of objects such that the objects of the sam...
This chapter presents a unified framework to generalize a number of fuzzy clustering algorithms to h...
Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number...
In a clustering problem, it would be better to use fuzzy clustering if there was an uncertainty in d...
Fuzzy clustering is useful to mine complex and multi-dimensional datasets, where the members have pa...
Cluster validation is a major issue in cluster analysis. Many existing validity indices do not perfo...
Abstract An improved cluster validity index for fuzzy clustering that is able to overcome three intr...
AbstractIn this paper, we define a validity measure for fuzzy criterion clustering which is a novel ...
Cluster analysis is an important tool in the exploration of large collections of data, revealing pat...
Clustering can be defined as the process of grouping physical or abstract objects into classes of si...
Because traditional fuzzy clustering validity indices need to specify the number of clusters and are...
Abstract: Finding the optimal cluster number and validating the partition results of a data set are ...
Abstract—Identification of correct number of clusters and the corresponding partitioning are two imp...
Cluster analysis is a multivariate statistical classification method, implying different methods and...
To measure the fuzziness of fuzzy sets, this paper introduces a distance-based and a fuzzy entropyba...
Abstract:- Clustering is a process of discovering groups of objects such that the objects of the sam...
This chapter presents a unified framework to generalize a number of fuzzy clustering algorithms to h...
Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number...
In a clustering problem, it would be better to use fuzzy clustering if there was an uncertainty in d...