Membership-based clustering of heterogeneous fuzzy data
- Gernot Herbst, Arne-Jens Hempel, Rainer Fletling, Steffen F. Bocklisch
- Corresponding Author
- Gernot Herbst
Available Online August 2011.
- https://doi.org/10.2991/eusflat.2011.119How to use a DOI?
- Fuzzy classification, clustering, pattern recognition, engineering geodesy.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 283 EP - 289 SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2011.119 DO - https://doi.org/10.2991/eusflat.2011.119 ID - Herbst2011/08 ER -