Membership-based clustering of heterogeneous fuzzy data

DOI

10.2991/eusflat.2011.119How to use a DOI?

Keywords

Fuzzy classification, clustering, pattern recognition, engineering geodesy.

Abstract

This article contributes to clustering and fuzzy modelling of data such that specific characteristics of each datum can be incorporated. Particularly, each object may exhibit an individual area of influence in its feature space, for which it is representative. For such objects, a similarity measure is introduced, which is used to modify common clustering algorithms to take each object's extent into account when finding clusters. A real-world example demonstrates the practical usability of the presented methods, which deliver results in accordance to findings of experts in that field.

Copyright

© 2011, 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  - CONF
AU  - Gernot Herbst
AU  - Arne-Jens Hempel
AU  - Rainer Fletling
AU  - Steffen F. Bocklisch
PY  - 2011/08
DA  - 2011/08
TI  - Membership-based clustering of heterogeneous fuzzy data
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)
PB  - Atlantis Press
SP  - 283
EP  - 289
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.119
DO  - 10.2991/eusflat.2011.119
ID  - Herbst2011/08
ER  -