Genetic Fuzzy Clustering by means of discovering membership functions

1997 ◽  
pp. 383-393 ◽  
Author(s):  
Meltem Turhan
Keyword(s):  
Fuzzy Clustering ◽  
Membership Functions

scholarly journals A Partitioning Based Algorithm to Fuzzy Tricluster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yongli Liu ◽  
Tengfei Yang ◽  
Lili Fu
Keyword(s):  
Membership Function ◽  
Fuzzy Clustering ◽  
Experimental Studies ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Membership Functions ◽  
Automatic Categorization ◽  
Dimensional Cube ◽  
Data Collections ◽  
Multiple Clusters

Fuzzy clustering allows an object to exist in multiple clusters and represents the affiliation of objects to clusters by memberships. It is extended to fuzzy coclustering by assigning both objects and features membership functions. In this paper we propose a new fuzzy triclustering (FTC) algorithm for automatic categorization of three-dimensional data collections. FTC specifies membership function for each dimension and is able to generate fuzzy clusters simultaneously on three dimensions. Thus FTC divides a three-dimensional cube into many little blocks which should be triclusters with strong coherent bonding among its members. The experimental studies onMovieLensdemonstrate the strength of FTC in terms of accuracy compared to some recent popular fuzzy clustering and coclustering approaches.

Fuzzy Clustering Based on Total Uncertainty Degree

2007 ◽  
Vol 11 (8) ◽  
pp. 897-904
Author(s):  
Tomohito Esaki ◽  
◽  
Tomonori Hashiyama ◽  
Yahachiro Tsukamoto ◽  
◽  
...  
Keyword(s):  
Membership Function ◽  
Fuzzy Clustering ◽  
Numerical Experiments ◽  
Data Distribution ◽  
Membership Functions ◽  
Objective Functions ◽  
Clustering Methods ◽  
Total Uncertainty ◽  
Possibilistic Clustering ◽  
Evidential Theory

Traditional Fuzzy c-Means (FCM) methods have probabilistic and additive restrictions that ∑ μ (x) = 1; the sum of membership values on the identified membership function is one. Possibilistic clustering methods identify membership functions without such constraints, but some parameters used in objective functions are difficult to understand and membership function shapes are independent of clusters estimated through possibilistic methods. We propose novel fuzzy clustering using a total uncertainty degree based on evidential theory with which we obtain nonadditive membership functions whose their shapes depend on data distribution, i.e., they mutually differ. Cluster meanings thus become easier to understand than in possibilistic methods and our proposal requires only one parameter “fuzzifier.” Numerical experiments demonstrated the feasibility of our proposal conducted.

2D hierarchical fuzzy clustering using kernel‐based membership functions

2016 ◽  
Vol 52 (3) ◽  
pp. 193-195 ◽  
Author(s):  
A. Proietti ◽  
L. Liparulo ◽  
M. Panella
Keyword(s):  
Fuzzy Clustering ◽  
Membership Functions

The Analysis of Traffic Flow State on Foggy Expressway Based on Fuzzy Clustering

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Jianjun Wang ◽  
Chenfeng Xie ◽  
Zhenwen Chang ◽  
Jingjing Zhang
Keyword(s):  
Traffic Flow ◽  
Fuzzy Clustering ◽  
Flow State
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