Cluster validation in linear fuzzy clustering of relational data from multi-cluster principal coordinate analysis view point

2009 ◽  
Author(s):  
Naoki Haga ◽  
Katsuhiro Honda ◽  
Akira Notsu ◽  
Hidetomo Ichihashi
Keyword(s):  
Fuzzy Clustering ◽  
Principal Coordinate Analysis ◽  
Relational Data ◽  
Cluster Validation ◽  
View Point

Fuzzy Cluster Validation Based on Fuzzy PCA-Guided Procedure

2013 ◽  
pp. 17-28
Author(s):  
K. Honda ◽  
A. Notsu ◽  
T. Matsui ◽  
H. Ichihashi
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy Cluster ◽  
Cluster Validation ◽  
View Point ◽  
Objective Functions ◽  
Geometrical Features ◽  
Validity Measures ◽  
Robust Cluster

Cluster validation is an important issue in fuzzy clustering research and many validity measures, most of which are motivated by intuitive justification considering geometrical features, have been developed. This paper proposes a new validation approach, which evaluates the validity degree of cluster partitions from the view point of the optimality of objective functions in FCM-type clustering. This approach makes it possible to evaluate the validity degree of robust cluster partitions, in which geometrical features are not available because of their possibilistic natures.

Fuzzy Cluster Validation Based on Fuzzy PCA-Guided Procedure

2011 ◽  
Vol 1 (1) ◽  
pp. 49-60 ◽  
Author(s):  
K. Honda ◽  
A. Notsu ◽  
T. Matsui ◽  
H. Ichihashi
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy Cluster ◽  
Cluster Validation ◽  
View Point ◽  
Objective Functions ◽  
Geometrical Features ◽  
Validity Measures ◽  
Robust Cluster

Cluster validation is an important issue in fuzzy clustering research and many validity measures, most of which are motivated by intuitive justification considering geometrical features, have been developed. This paper proposes a new validation approach, which evaluates the validity degree of cluster partitions from the view point of the optimality of objective functions in FCM-type clustering. This approach makes it possible to evaluate the validity degree of robust cluster partitions, in which geometrical features are not available because of their possibilistic natures.

Robust fuzzy clustering of relational data

2002 ◽  
Vol 10 (6) ◽  
pp. 713-727 ◽  
Author(s):  
R.N. Dave ◽  
S. Sen
Keyword(s):  
Fuzzy Clustering ◽  
Relational Data

GIVING FUZZINESS TO SPATIAL CLUSTERS: A NEW INDEX FOR CHOOSING THE OPTIMAL NUMBER OF CLUSTERS

2013 ◽  
Vol 22 (03) ◽  
pp. 1350009 ◽  
Author(s):  
GEORGE GREKOUSIS
Keyword(s):  
Fuzzy Clustering ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Fuzzy Cluster ◽  
Cluster Validation ◽  
Number Of Clusters ◽  
A Value ◽  
Membership Value ◽  
Optimal Number Of Clusters

Choosing the optimal number of clusters is a key issue in cluster analysis. Especially when dealing with more spatial clustering, things tend to be more complicated. Cluster validation helps to determine the appropriate number of clusters present in a dataset. Furthermore, cluster validation evaluates and assesses the results of clustering algorithms. There are numerous methods and techniques for choosing the optimal number of clusters via crisp and fuzzy clustering. In this paper, we introduce a new index for fuzzy clustering to determine the optimal number of clusters. This index is not another metric for calculating compactness or separation among partitions. Instead, the index uses several existing indices to give a degree, or fuzziness, to the optimal number of clusters. In this way, not only do the objects in a fuzzy cluster get a membership value, but the number of clusters to be partitioned is given a value as well. The new index is used in the fuzzy c-means algorithm for the geodemographic segmentation of 285 postal codes.

Entropy-Regularized Fuzzy Clustering for Non-Euclidean Relational Data and Indefinite Kernel Data

2012 ◽  
Vol 16 (7) ◽  
pp. 784-792 ◽  
Author(s):  
Yuchi Kanzawa ◽  
Keyword(s):  
Fuzzy Clustering ◽  
Numerical Experiments ◽  
Standard Approach ◽  
Relational Data ◽  
Data Types ◽  
Indefinite Kernel ◽  
Clustering Approach ◽  
Theoretical Results

In this paper, an entropy-regularized fuzzy clustering approach for non-Euclidean relational data and indefinite kernel data is developed that has not previously been discussed. It is important because relational data and kernel data are not always Euclidean and positive semi-definite, respectively. It is theoretically determined that an entropy-regularized approach for both non-Euclidean relational data and indefinite kernel data can be applied without using a β-spread transformation, and that two other options make the clustering results crisp for both data types. These results are in contrast to those from the standard approach. Numerical experiments are employed to verify the theoretical results, and the clustering accuracy of three entropy-regularized approaches for non-Euclidean relational data, and three for indefinite kernel data, is compared.

scholarly journals Evolutionary fuzzy clustering of relational data

2011 ◽  
Vol 412 (42) ◽  
pp. 5854-5870 ◽  
Author(s):  
Danilo Horta ◽  
Ivan C. de Andrade ◽  
Ricardo J.G.B. Campello
Keyword(s):  
Fuzzy Clustering ◽  
Relational Data
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