Results 251 to 260 of about 376,662 (290)

A fuzzy clustering model of data and fuzzy c-means

Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2002
The multiple prototype fuzzy clustering model (FCMP), introduced by Nascimento, Mirkin and Moura-Pires (1999), proposes a framework for partitional fuzzy clustering which suggests a model of how the data are generated from a cluster structure to be identified.
Susana Nascimento, Boris G. Mirkin, Fernando Moura-Pires   +2 more
openaire   +1 more source

Fuzzy C-Means Stereo Segmentation

2015
An extension to the popular fuzzy c-means clustering method is proposed by introducing an additional disparity cue. The creation of the fuzzy clusters is driven by a degree of the stereo match and thus it enables to separate the objects not only by their different colours but also on their different spatial depth.
Michal Krumnikl, Eduard Sojka, Jan Gaura
openaire   +1 more source

Relative entropy fuzzy c-means clustering

Information Sciences, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Marzie Zarinbal, M. H. Fazel Zarandi, I. Burhan Türksen   +2 more
openaire   +3 more sources

Complex fuzzy c-means algorithm

Artificial Intelligence Review, 2011
In this paper a new clustering algorithm is presented: A complex-based Fuzzy c-means (CFCM) algorithm. While the Fuzzy c-means uses a real vector as a prototype characterizing a cluster, the CFCM's prototype is generalized to be a complex vector (complex center). CFCM uses a new real distance measure which is derived from a complex one. CFCM's formulas
openaire   +1 more source

Rough C-means and Fuzzy Rough C-means for Colour Quantisation

Fundamenta Informaticae, 2012
Colour quantisation algorithms are essential for displaying true colour images using a limited palette of distinct colours. The choice of a good colour palette is crucial as it directly determines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best ...
Gerald Schaefer, Qinghua Hu, Huiyu Zhou 0001, James F. Peters, Aboul Ella Hassanien   +4 more
openaire   +2 more sources

Generalized fuzzy c-means algorithms

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2000
This paper proposes generalized fuzzy c-means (FCM) algorithms. The clustering problem is formulated as a constrained minimization problem, whose solution depends on the selection of a constraint function that satisfies certain conditions. If the constraint function is proportional to the generalized mean of the membership values, the solution of this ...
openaire   +2 more sources

Incremental Kernel Fuzzy c-Means

2012
The size of everyday data sets is outpacing the capability of computational hardware to analyze these data sets. Social networking and mobile computing alone are producing data sets that are growing by terabytes every day. Because these data often cannot be loaded into a computer’s working memory, most literal algorithms (algorithms that require access
Timothy C. Havens, James C. Bezdek, Marimuthu Palaniswami   +2 more
openaire   +2 more sources

Suppressed fuzzy c-means clustering algorithm

Pattern Recognition Letters, 2003
Summary: Based on the defect of rival checked fuzzy \(c\)-means clustering algorithm, a new algorithm: suppressed fuzzy \(c\)-means clustering algorithm is proposed. The new algorithm overcomes the shortcomings of the original algorithm, establishes more natural and more reasonable relationships between hard \(c\)-means clustering algorithm and fuzzy \(
Jiu-Lun Fan 0001, Wen-Zhi Zhen, Weixin Xie   +2 more
openaire   +2 more sources

Fuzzy C-Means in High Dimensional Spaces

International Journal of Fuzzy System Applications, 2011
High dimensions have a devastating effect on the FCM algorithm and similar algorithms. One effect is that the prototypes run into the centre of gravity of the entire data set. The objective function must have a local minimum in the centre of gravity that causes FCM’s behaviour. In this paper, examine this problem.
Roland Winkler, Frank Klawonn, Rudolf Kruse   +2 more
openaire   +1 more source

Variable Width Rough-Fuzzy c-Means

2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2017
The richness of soft clustering algorithms in the scientific literature reflects from one side the complexity of the underlying problem and from the other the many attempts that have been made to preserve interpretability while modeling vagueness through different theories.
Ferone, Alessio, Galletti, Ardelio, Maratea, Antonio   +2 more
openaire   +2 more sources