Proceedings ArticleDOI
Cluster validity index: Comparative study and a new validity index with high performance
TLDR
A new validity index named Vcw is proposed for the fuzzy c-means algorithm and the performance of eight fairly recent cluster validity indexes are compared to select the best one between them that could give us the optimal number of clusters in the presence of a high overlap between the clusters.Abstract:
Cluster validity indexes are used to identify the best partitioning in a dataset from the results of a clustering algorithm. The overlap phenomenon is a source of failure for most of these validity indexes.In this work, we propose a new validity index named Vcw for the fuzzy c-means algorithm and we also propose to compare the performance of eight fairly recent cluster validity indexes with our new index on artificial and real data, in order to select the best one between them that could give us the optimal number of clusters in the presence of a high overlap between the clusters.read more
Citations
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Journal Article
A New Cluster Validity Index for Fuzzy Clustering
TL;DR: A new validity index for determining the number of clusters is proposed, based on a novel way of combining cohesion and discrepancy, which shows clearly the efficiency of the new index under the condition of overlapping clusters.
Journal ArticleDOI
Uncertainty clustering internal validity assessment using Fréchet distance for unsupervised learning
N Orozco Rendon,Jhony H. Giraldo,Thierry Bouwmans,Susana Rodríguez-Buriticá,Edison J Ramírez,Claudia Isaza +5 more
TL;DR: In this paper , uncertainty fingerprints based on Type-2 fuzzy Gaussian Mixture Models (T2FGMM) and the Fréchet distance between clusters are used to assess the certainty of a well-defined partition.
References
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Journal ArticleDOI
Pattern classification problems and fuzzy sets
TL;DR: A unified presentation of classical clustering algorithms is proposed both for the hard and fuzzy pattern classification problems, and two coefficients that measure the “degree of non-fuzziness” of the partition are proposed.
Journal ArticleDOI
Editorial: WB-index: A sum-of-squares based index for cluster validity
Qinpei Zhao,Pasi Fränti +1 more
TL;DR: A more thorough comparison of 12 internal indices is conducted and a summary of the experimental performance of different indices is provided and the sum-of-squares based indices are introduced into automatic keyword categorization, where the indices are specially defined for determining the number of clusters.
Proceedings ArticleDOI
Improved validation index for fuzzy clustering
TL;DR: A new validation index for fuzzy clustering is proposed in order to eliminate the monotonically decreasing tendency as the number of clusters approaches to thenumber of data points and avoid the numerical instability of validation index when fuzzy weighting exponent increases.
Journal ArticleDOI
Generalized Possibilistic Fuzzy C-Means with novel cluster validity indices for clustering noisy data
TL;DR: A fuzzy clustering algorithm is presented for noisy data and average error of GPFCM and its simplified forms are about 80% smaller than those of FCM, PCM, and PFCM, however, GP FCM demands higher computational costs due to nonlinear updating equations.
Journal Article
An Exponential Cluster Validity Index for Fuzzy Clustering with Crisp and Fuzzy Data
TL;DR: A new cluster validity index, called the ECAS-index, contains exponential compactness and separation measures that indicate homogeneity within clusters and heterogeneity between clusters, respectively.