๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Cluster validity for fuzzy criterion clustering

โœ Scribed by A.O. Esogbue; Baoding Liu

Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
358 KB
Volume
37
Category
Article
ISSN
0898-1221

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

A new cluster-validity for fuzzy cluster
A new cluster-validity for fuzzy clustering
โœ N. Zahid; M. Limouri; A. Essaid ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 136 KB

Fuzzy cluster-validity criterion tends to evaluate the quality of fuzzy c-partitions produced by fuzzy clustering algorithms. Many functions have been proposed. Some methods use only the properties of fuzzy membership degrees to evaluate partitions. Others techniques combine the properties of member

Validity index for crisp and fuzzy clust
Validity index for crisp and fuzzy clusters
โœ Malay K. Pakhira; Sanghamitra Bandyopadhyay; Ujjwal Maulik ๐Ÿ“‚ Article ๐Ÿ“… 2004 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 242 KB
A stability based validity method for fu
A stability based validity method for fuzzy clustering
โœ M. Falasconi; A. Gutierrez; M. Pardo; G. Sberveglieri; S. Marco ๐Ÿ“‚ Article ๐Ÿ“… 2010 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 657 KB

An important goal in cluster analysis is the internal validation of results using an objective criterion. Of particular relevance in this respect is the estimation of the optimum number of clusters capturing the intrinsic structure of your data. This paper proposes a method to determine this optimum

Spatial Models for Fuzzy Clustering
Spatial Models for Fuzzy Clustering
โœ Dzung L. Pham ๐Ÿ“‚ Article ๐Ÿ“… 2001 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 450 KB

A novel approach to fuzzy clustering for image segmentation is described. The fuzzy C-means objective function is generalized to include a spatial penalty on the membership functions. The penalty term leads to an iterative algorithm that is only slightly different from the original fuzzy C-means alg