Results 261 to 270 of about 375,352 (295)

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

Interval-Valued Fuzzy c-Means Algorithm and Interval-Valued Density-Based Fuzzy c-Means Algorithm

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020
Most of the time membership value in the fuzzy set cannot be exactly defined. Interval-valued fuzzy set (IVFS) is a special type of type-2 fuzzy sets which represents the membership value of the fuzzy set as an interval. IVFS assumes that membership interval can better represent the uncertainty in the data.
Ayush K. Varshney, Priyanka Mehra, Pranab K. Muhuri, Q. M. Danish Lohani   +3 more
openaire   +1 more source

Intuitive fuzzy c-means algorithm

2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2009
Fuzzy c-means (FCM) is one of the most widely used clustering algorithms and assigns memberships to which are inversely related to the relative distance to the point prototypes that are cluster centers in the FCM model. In order to overcome the problem of outliers in data, several models including possibilistic c-means (PCM) and possibilistic-fuzzy c ...
openaire   +1 more source

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   +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

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
Havens, TC, Bezdek, JC, Palaniswami, M
openaire   +2 more sources

Fuzzy C-Means on Metric Lattice

Automatic Control and Computer Sciences, 2020
This work proposes a new clustering algorithm named FINFCM by converting original data into fuzzy interval number (FIN) firstly, then it proofs F that denotes the collection of FINs is a lattice and introduce a novel metric distance based on the results from lattice theory as well as combining them with Fuzzy c-means clustering.
X. Meng, M. Liu, H. Zhou, J. Wu, F. Xu, Q. Wu   +5 more
openaire   +1 more source

A weighted fuzzy c-means clustering model for fuzzy data

Computational Statistics & Data Analysis, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
D'URSO, Pierpaolo, GIORDANI, Paolo
openaire   +2 more sources

Generalized Fuzzy C-Means Clustering Algorithm With Improved Fuzzy Partitions

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009
The fuzziness index m has important influence on the clustering result of fuzzy clustering algorithms, and it should not be forced to fix at the usual value m = 2. In view of its distinctive features in applications and its limitation in having m = 2 only, a recent advance of fuzzy clustering called fuzzy c-means clustering with improved fuzzy ...
Lin, Zhu, Fu-Lai, Chung, Shitong, Wang
openaire   +2 more sources

Fuzzy C-means and fuzzy swarm for fuzzy clustering problem

Expert Systems with Applications, 2011
Fuzzy clustering is an important problem which is the subject of active research in several real-world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement.
Hesam Izakian, Ajith Abraham
openaire   +1 more source