Clustering feature vectors with mixed numerical and categorical attributes

DOI

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.

Copyright

© 2009, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - R.K. Brouwer
PY  - 2008
DA  - 2008/12/01
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  - 10.2991/ijcis.2008.1.4.1
ID  - Brouwer2008
ER  -