Journal ArticleDOI
A validity measure for fuzzy clustering
X.L. Xie,Gerardo Beni +1 more
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TLDR
The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data.Abstract:
The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data. This function depends on the data set, geometric distance measure, distance between cluster centroids and more importantly on the fuzzy partition generated by any fuzzy algorithm used. The function is mathematically justified via its relationship to a well-defined hard clustering validity function, the separation index for which the condition of uniqueness has already been established. The performance of this validity function compares favorably to that of several others. The application of this validity function to color image segmentation in a computer color vision system for recognition of IC wafer defects which are otherwise impossible to detect using gray-scale image processing is discussed. >read more
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
On cluster validity for the fuzzy c-means model
Nikhil R. Pal,James C. Bezdek +1 more
TL;DR: Limitation analysis indicates, and numerical experiments confirm, that the Fukuyama-Sugeno index is sensitive to both high and low values of m and may be unreliable because of this, and calculations suggest that the best choice for m is probably in the interval [1.5, 2.5], whose mean and midpoint, m=2, have often been the preferred choice for many users of FCM.
Journal ArticleDOI
Color image segmentation: advances and prospects
TL;DR: This survey provides a summary of color image segmentation techniques available now based on monochrome segmentation approaches operating in different color spaces and some novel approaches such as fuzzy method and physics-based method are investigated.
Journal ArticleDOI
Survey: Graph clustering
TL;DR: This survey overviews the definitions and methods for graph clustering, that is, finding sets of ''related'' vertices in graphs, and presents global algorithms for producing a clustering for the entire vertex set of an input graph.
References
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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.
Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
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.
Book
Cluster Analysis
TL;DR: This fourth edition of the highly successful Cluster Analysis represents a thorough revision of the third edition and covers new and developing areas such as classification likelihood and neural networks for clustering.
Journal ArticleDOI
Well-Separated Clusters and Optimal Fuzzy Partitions
TL;DR: In this article, two separation indices for partitions P = {X1, …, Xk} of a finite data set X in a general inner product space are considered, and it is shown that as the indices of p' increase without bound, the characteristic functions of Xi' in P' are approximated more and more closely by the membership functions in fuzzy partitions which minimize certain fuzzy extensions of the k-means squared error criterion function.