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Journal ArticleDOI

Validating fuzzy partitions obtained through c-shells clustering

Rajesh N. Dave
- 15 May 1996 - 
- Vol. 17, Iss: 6, pp 613-623
TLDR
Validation of fuzzy partitions induced through c-shells clustering is considered, and a new set of indices are shown to be capable of validating the structure characterized by the shell clustering algorithms.
About
This article is published in Pattern Recognition Letters.The article was published on 1996-05-15. It has received 264 citations till now. The article focuses on the topics: Fuzzy clustering & Cluster analysis.

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Citations
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Journal ArticleDOI

On Clustering Validation Techniques

TL;DR: The fundamental concepts of clustering are introduced while it surveys the widely known clustering algorithms in a comparative way and the issues that are under-addressed by the recent algorithms are illustrated.
Journal ArticleDOI

Cluster validity methods: part I

TL;DR: This paper presents a review of the clustering validity and methods based on external and internal criteria and discusses the cluster validity approaches based on internal and external criteria.
Journal ArticleDOI

Clustering validity checking methods: part II

TL;DR: The paper illustrates the issues that are under-addressed by the recent approaches to clustering validity checking approaches and proposes the research directions in the field.
Journal ArticleDOI

On fuzzy cluster validity indices

TL;DR: The fundamental concepts of cluster validity are introduced, and a review of fuzzy cluster validity indices available in the literature are presented, and extensive comparisons of the mentioned indices are conducted in conjunction with the Fuzzy C-Means clustering algorithm.
Journal ArticleDOI

A cluster validity index for fuzzy clustering

TL;DR: The results of comparative study show that the proposed PCAES index has high ability in producing a good cluster number estimate and in addition, it provides a new point of view for cluster validity in a noisy environment.
References
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Journal ArticleDOI

A mathematical theory of communication

TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Book

Pattern Recognition with Fuzzy Objective Function Algorithms

TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Book

Pattern classification and scene analysis

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.