TY  - CONF
ID  - Borgelt_et_al_2005a
T1  - Fuzzy Learning Vector Quantization with Size and Shape Parameters
A1  - Borgelt, Christian
A1  - Nürnberger, Andreas
A1  - Kruse, Rudolf
TI  - Proc. 15th IEEE Int. Conf. on Fuzzy Systems, 2005 (FUZZ-IEEE'05, Reno, NV), CD-ROM
Y1  - 2005
SP  - 195
EP  - 200
PB  - IEEE Press
AD  - Piscataway, NJ, USA
UR  - http://borgelt.net/papers/fieee_05.pdf
M2  - doi: 10.1109/fuzzy.2005.1452392
N2  - We study an extension of fuzzy learning vector quantization that draws on ideas from the more sophisticated approaches to fuzzy clustering, enabling us to find fuzzy clusters of ellipsoidal shape and differing size with a competitive learning scheme. This approach may be seen as a kind of online fuzzy clustering, which can have advantages w.r.t. the execution time of the clustering algorithm. We demonstrate the usefulness of our approach by applying it to document collections, which are, in general, difficult to cluster due to the high number of dimensions and the special distribution characteristics of the data
ER  -