FCM-Type Fuzzy Clustering of Mixed Databases Considering Nominal Variable Quantification

2007 ◽  
Vol 11 (2) ◽  
pp. 162-167 ◽  
Author(s):  
Katsuhiro Honda ◽  
◽  
Ryo Uesugi ◽  
Hidetomo Ichihashi
Keyword(s):  
Objective Function ◽  
Linear Combination ◽  
Fuzzy Clustering ◽  
Categorical Data ◽  
Clustering Algorithm ◽  
Cluster Structure ◽  
Cost Functions ◽  
Fuzzy Partition ◽  
Clustering Criterion ◽  
The Relationship

This paper proposes a clustering algorithm that performs FCM-type clustering of datasets including categorical data. The proposed algorithm iterates categorical data quantification in FCE clustering so that quantified scores suit the current fuzzy partition. The objective function is the linear combination of two cost functions, i.e., the objective function of FCE clustering and the clustering criterion of quantified category scores. Because quantified category scores are assigned considering the relationship among categories, they are useful for interpreting the cluster structure.

A novel fuzzy clustering algorithm with between-cluster information for categorical data

2013 ◽  
Vol 215 ◽  
pp. 55-73 ◽  
Author(s):  
Liang Bai ◽  
Jiye Liang ◽  
Chuangyin Dang ◽  
Fuyuan Cao
Keyword(s):  
Fuzzy Clustering ◽  
Categorical Data ◽  
Clustering Algorithm ◽  
Fuzzy Clustering Algorithm

FUZZY CLUSTERING WITH WEIGHTING OF DATA VARIABLES

2000 ◽  
Vol 08 (06) ◽  
pp. 735-746 ◽  
Author(s):  
ANNETTE KELLER ◽  
FRANK KLAWONN
Keyword(s):  
Objective Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Technique ◽  
Fuzzy C Means ◽  
Or Groups ◽  
Fuzzy C Means Clustering ◽  
Influence Parameter ◽  
Individual Variables ◽  
Single Data

We introduce an objective function-based fuzzy clustering technique that assigns one influence parameter to each single data variable for each cluster. Our method is not only suited to detect structures or groups of data that are not uniformly distributed over the structure's single domains, but gives also information about the influence of individual variables on the detected groups. In addition, our approach can be seen as a generalization of the well-known fuzzy c-means clustering algorithm.

Identifying the Mastery Concepts in Linear Algebra by Using FCM-CM Algorithm

2010 ◽  
Vol 44-47 ◽  
pp. 3897-3901
Author(s):  
Hsiang Chuan Liu ◽  
Yen Kuei Yu ◽  
Jeng Ming Yih ◽  
Chin Chun Chen
Keyword(s):  
Objective Function ◽  
Linear Algebra ◽  
Fuzzy Clustering ◽  
Distance Function ◽  
Mahalanobis Distance ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Euclidean Distance Function ◽  
Fuzzy C Means Algorithm

Euclidean distance function based fuzzy clustering algorithms can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm were developed to detect non-spherical structural clusters by employing Mahalanobis distance in objective function, however, both of them need to add some constrains for Mahalanobis distance. In this paper, the authors’ improved Fuzzy C-Means algorithm based on common Mahalanobis distance (FCM-CM) is used to identify the mastery concepts in linear algebra, for comparing the performances with other four partition algorithms; FCM-M, GG, GK, and FCM. The result shows that FCM-CM has better performance than others.

Quantification of Multivariate Categorical Data Considering Typicality of Item

2007 ◽  
Vol 11 (1) ◽  
pp. 35-39 ◽  
Author(s):  
Chi-Hyon Oh ◽  
◽  
Katsuhiro Honda ◽  
Hidetomo Ichihashi ◽  
Keyword(s):  
Numerical Experiment ◽  
Objective Function ◽  
Fuzzy Clustering ◽  
Categorical Data ◽  
The Other ◽  
Additional Parameter ◽  
Homogeneity Analysis ◽  
Degree Of Membership ◽  
Multivariate Categorical

We propose simultaneously applying homogeneity analysis and fuzzy clustering that simultaneously partitions individuals and items in categorical multivariate datasets. This objective function includes two types of memberships. One is conventional membership representing the degree of membership of each individual in each cluster. The other is an additional parameter that represents typicality of item. A numerical experiment demonstrates that our proposal is useful in quantifying categorical data, taking the typicality of each item into account.

FUZZY CLUSTERING ALGORITHM FOR INTEGRATING MULTISCALE SPATIAL CONTEXT IN IMAGE SEGMENTATION BY HIDDEN MARKOV RANDOM FIELD MODELS

2013 ◽  
Vol 27 (03) ◽  
pp. 1355005 ◽  
Author(s):  
GUO-YING LIU ◽  
AI-MIN WANG
Keyword(s):  
Random Field ◽  
Objective Function ◽  
Fuzzy Clustering ◽  
Markov Random Field ◽  
Clustering Algorithm ◽  
Hidden Markov ◽  
Wavelet Coefficient ◽  
Fuzzy Clustering Algorithm ◽  
Markov Random ◽  
Hidden Markov Random Field

In this study, a fuzzy clustering algorithm, MRHMRF-FCM, is proposed to capture and utilize the multiscale spatial constrains by employing multiresolution representations for the label image and the observed image in wavelet domain. In this algorithm, the inner-scale and inter-scale spatial constrains, respectively modeled by the hidden Markov random field models, serve as the penalization terms for the objective function of the FCM algorithm. On each scale, the improved objective function is optimized by taking advantage of Lagrange multipliers, and the final label of wavelet coefficient is determined by iteratively updating the membership degree and cluster centers. The experimental results on synthetic images, natural scenery color images and remote sensed images show that the proposed algorithm obtains much better segmentation results, such as accurately differentiating different regions and being immune to noise.

Fuzzy Clustering for Detecting Linear Structures with Different Dimensions

1999 ◽  
Vol 3 (1) ◽  
pp. 13-20 ◽  
Author(s):  
Kazutaka Umayahara ◽  
◽  
Yoshiteru Nakamori ◽  
Sadaaki Miyamoto ◽  
Keyword(s):  
Objective Function ◽  
Simultaneous Determination ◽  
Fuzzy Clustering ◽  
Linear Structures ◽  
Fuzzy Partition ◽  
Recent Interest ◽  
Different Dimensions ◽  
Different Shapes ◽  
Noise Cluster

One recent interest in fuzzy clustering is the simultaneous determination of a fuzzy partition of a given dataset and parameters of assumed models having different shapes that explain partitioned datasets. We propose an objective function to detect linear varieties with different dimensionalities. The noise cluster suggested by Dave is introduced. Since this is not all-purpose method, some techniques are suggested using artificial examples to show how to implement clustering successfully.

Classification of Ship’ Magnetic Field and Feature Selection Based on the Improved Weighted Fuzzy Clustering Algorithm

2013 ◽  
Vol 401-403 ◽  
pp. 1353-1357
Author(s):  
Wu Di Wen ◽  
Zhong Le Liu ◽  
Zhi Qiang Zhang
Keyword(s):  
Magnetic Field ◽  
Feature Selection ◽  
Objective Function ◽  
Fuzzy Clustering ◽  
Field Data ◽  
Clustering Algorithm ◽  
Magnetic Field Data ◽  
Fuzzy Clustering Algorithm ◽  
Improved Algorithm

Magnetic field data of ship has three-component,and traditional weighted fuzzy clustering algorithm(FCA) can’t deal with the three-component data. We improve the traditional FCA by changing the objective function and added weights calculation of three-component of magnetic field in the function.Give the equation to compute the weights of three-component.Put forward new steps for improved algorithm.Use ships’ data to test the improved algorithm and giving the conclusion.

Inner-Cluster-Structure Reconstruction Based Transfer Fuzzy Clustering and MRI Segmentation Applications

2020 ◽  
Vol 10 (7) ◽  
pp. 1575-1583
Author(s):  
Yang Ding ◽  
Leyuan Zhou
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Cluster Structure ◽  
Mr Images ◽  
Target Domain ◽  
Mri Segmentation ◽  
Structure Reconstruction ◽  
Domain Learning ◽  
Synthetic Datasets ◽  
Novel Algorithm

MRI automatically segmentation is very useful in clinical diagnosis. However, in some cases, MR images are contaminated by noises or lose pixels and then become sparse such that automatically segmentation by classical algorithms becomes difficult or impossible. In this study, we propose a transfer fuzzy clustering algorithm based on inner-cluster-structure reconstruction. Firstly, we use all objects to represent the inner cluster structure by assigning weights to all object. Secondly, in order to reconstruct the cluster structure in the target domain in which objects distribute sparsely or are contaminated by noises, we joint the two domains together and recalculate the weights of all objects in the target domain. Thirdly, the updated weights in the target domain are considered as transfer knowledge that is used for guiding the target domain learning. Experimental results on MR images and synthetic datasets indicate our novel algorithm achieves the best performance comparing with other similar algorithms.

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