Journal ArticleDOI
A possibilistic approach to clustering
TLDR
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.Abstract:
The clustering problem is cast in the framework of possibility theory. The approach differs from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values can be interpreted as degrees of possibility of the points belonging to the classes, i.e., the compatibilities of the points with the class prototypes. 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. The advantages of the resulting family of possibilistic algorithms are illustrated by several examples. >read more
Citations
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Journal ArticleDOI
Survey of clustering algorithms
Rui Xu,Donald C. Wunsch +1 more
TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts 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
Review: A review of novelty detection
TL;DR: This review aims to provide an updated and structured investigation of novelty detection research papers that have appeared in the machine learning literature during the last decade.
Book
Fuzzy Modeling for Control
TL;DR: Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering point of view and focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements, and on the design of nonlinear controllers based on fuzzy models.
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.
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
Fuzzy sets as a basis for a theory of possibility
TL;DR: The theory of possibility described in this paper is related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable.
Book
Computer and Robot Vision
TL;DR: This two-volume set is an authoritative, comprehensive, modern work on computer vision that covers all of the different areas of vision with a balanced and unified approach.
Book
Fuzzy Sets, Uncertainty and Information
George J. Klir,Tina A. Folger +1 more
TL;DR: The fuzzy sets uncertainty and information is one book that the authors really recommend you to read, to get more solutions in solving this problem.