International Journal of Computational Intelligence Systems

Volume 1, Issue 4, December 2008, Pages 285 - 298

Clustering feature vectors with mixed numerical and categorical attributes

Authors
R.K. Brouwer
Corresponding Author
R.K. Brouwer
Available Online 2 January 2009.
DOI
https://doi.org/10.2991/ijcis.2008.1.4.1How to use a DOI?
Keywords
Fuzzy clustering, gradient descent, categorical, nominal clustering, fuzzy c-means
Abstract
This paper describes a method for finding a fuzzy membership matrix in case of numerical and categorical features. The set of feature vectors with mixed features is mapped to a set of feature vectors with only real valued components with the condition that the new set of vectors has the same proximity matrix as the original feature vectors. This new set of vectors is then clustered using fuzzy c-means. Simulations show the method to be very effective in comparison with other methods.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
1 - 4
Pages
285 - 298
Publication Date
2009/01
ISSN
1875-6883
DOI
https://doi.org/10.2991/ijcis.2008.1.4.1How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - R.K. Brouwer
PY  - 2009
DA  - 2009/01
TI  - Clustering feature vectors with mixed numerical and categorical attributes
JO  - International Journal of Computational Intelligence Systems
SP  - 285
EP  - 298
VL  - 1
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2008.1.4.1
DO  - https://doi.org/10.2991/ijcis.2008.1.4.1
ID  - Brouwer2009
ER  -