Previously, eight popular information-theoretic based cluster validity indices have been generalized and tested for probabilistic partitions built by the expectation-maximization (EM) algorithm for the Gaussian mixture model. But the analysis was limited to probabilistic clusters and there were limited explanations for differences in the performance of the indices. In this paper, we extend the tests to partitions found by fuzzy c-Means (FCM) and provide further explanations and insights about the performance of these indices. Of the eight generalized indices, we advocate a normalized version of the soft mutual information cluster validity index (NMIsM) as the best overall choice, as it outperforms the other seven indices for both FCM and EM...
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04277-5_24Pro...
Abstract. In this paper, we examine the performance of fuzzy clustering algorithms as the major tech...
Cluster analysis is a multivariate statistical classification method, implying different methods and...
Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number...
Fuzzy clustering is useful to mine complex and multi-dimensional datasets, where the members have pa...
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 ...
2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 --16 September 2...
Cluster validation is a major issue in cluster analysis. Many existing validity indices do not perfo...
Clustering can be defined as the process of grouping physical or abstract objects into classes of si...
Abstract An improved cluster validity index for fuzzy clustering that is able to overcome three intr...
Invited talk: To compare clustering partitions, Rand index (RI) and Adjusted Rand index (ARI) are co...
Many external validity indices for comparing different clusterings of the same set of objects are ov...
Clustering validity evaluation is a key part in clustering process. To adapt the complex data struct...
Since clustering is an unsupervised method and there is no a-priori indication for the actual number...
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04277-5_24Pro...
Abstract. In this paper, we examine the performance of fuzzy clustering algorithms as the major tech...
Cluster analysis is a multivariate statistical classification method, implying different methods and...
Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number...
Fuzzy clustering is useful to mine complex and multi-dimensional datasets, where the members have pa...
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 ...
2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 --16 September 2...
Cluster validation is a major issue in cluster analysis. Many existing validity indices do not perfo...
Clustering can be defined as the process of grouping physical or abstract objects into classes of si...
Abstract An improved cluster validity index for fuzzy clustering that is able to overcome three intr...
Invited talk: To compare clustering partitions, Rand index (RI) and Adjusted Rand index (ARI) are co...
Many external validity indices for comparing different clusterings of the same set of objects are ov...
Clustering validity evaluation is a key part in clustering process. To adapt the complex data struct...
Since clustering is an unsupervised method and there is no a-priori indication for the actual number...
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04277-5_24Pro...
Abstract. In this paper, we examine the performance of fuzzy clustering algorithms as the major tech...
Cluster analysis is a multivariate statistical classification method, implying different methods and...