Robust fuzzy clustering of relational data

2002 ◽  
Vol 10 (6) ◽  
pp. 713-727 ◽  
Author(s):  
R.N. Dave ◽  
S. Sen
Author(s):  
Yuchi Kanzawa ◽  

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.


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

Author(s):  
Mihail Popescu

In this chapter the author presents a fuzzy clustering methodology that can be employed for large relational datasets. Relational data is an N×N matrix that consists of pair-wise dissimilarities among N objects. Large relational datasets are encountered in many domains such as psychology or medical informatics, but they are abundant in bioinformatics where gene products are compared to each other based on various characteristics such as DNA or amino acid sequence. The fuzzy clustering methodology is exemplified on a set of about 30,000 human gene products.


2010 ◽  
Vol 43 (5) ◽  
pp. 1964-1974 ◽  
Author(s):  
Jian-Ping Mei ◽  
Lihui Chen

Sign in / Sign up

Export Citation Format

Share Document