Proceedings ArticleDOI
A mixed c-means clustering model
Nikhil R. Pal,Kuhu Pal,James C. Bezdek +2 more
- Vol. 1, pp 11-21
Reads0
Chats0
TLDR
F fuzzy-possibilistic c-means is proposed, and it is shown that FPCM solves the noise sensitivity defect of FCM, and also overcomes the coincident clusters problem of PCM.Abstract:
We justify the need for computing both membership and typicality values when clustering unlabeled data. Then we propose a new model called fuzzy-possibilistic c-means (FPCM). Unlike the fuzzy and possibilistic c-means (FCM/PCM) models, FPCM simultaneously produces both memberships and possibilities, along with the usual point prototypes or cluster centers for each cluster We show that FPCM solves the noise sensitivity defect of FCM, and also overcomes the coincident clusters problem of PCM. Then we derive first order necessary conditions for extrema of the PFCM objective function, and use them as the basis for a standard alternating optimization approach to finding local minima. Three numerical examples are given that compare FCM to FPCM. Our calculations show that FPCM compares favorably to FCM.read more
Citations
More filters
Journal ArticleDOI
A possibilistic fuzzy c-means clustering algorithm
TL;DR: A new model called possibilistic-fuzzy c-means (PFCM) model, which solves the noise sensitivity defect of FCM, overcomes the coincident clusters problem of PCM and eliminates the row sum constraints of FPCM.
Book
Handbook of computer vision and applications
TL;DR: The aim of this book is to provide a history of 3-D Imaging and its applications in Computer Vision up to and including the 1990s, as well as some of the techniques used in that period.
Journal ArticleDOI
3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models
TL;DR: The brain is segmented using a new approach, robust to the presence of tumors, based on a combination of a deformable model and spatial relations, leading to a precise segmentation of the tumors.
BookDOI
Advances in Computational Intelligence
TL;DR: This work proposes customized model ensembles on demand, inspired by Lazy Learning, which finds the most relevant models from a DB of models, using their meta-information, and creates an ensemble, which produces an output that is a weighted interpolation or extrapolation of the outputs of the models ensemble.
Journal ArticleDOI
Improved possibilistic C-means clustering algorithms
Jiangshe Zhang,Yiu-Wing Leung +1 more
TL;DR: The numerical results demonstrate that the improved algorithms modified and improved can determine proper clusters and they can realize the advantages of the possibilistic approach.
References
More filters
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
Applied Multivariate Statistical Analysis
R. A. Johnson,Dean W. Wichern +1 more
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.
Journal ArticleDOI
Applied Multivariate Statistical Analysis.
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.
Journal ArticleDOI
A possibilistic approach to clustering
TL;DR: An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.
Proceedings ArticleDOI
Fuzzy clustering with a fuzzy covariance matrix
TL;DR: Experimental results are presented which indicate that more accurate clustering may be obtained by using fuzzy covariances, a natural approach to fuzzy clustering.