Cluster Tendency Assessment for Fuzzy Clustering of Incomplete Data

DOI

10.2991/eusflat.2011.136How to use a DOI?

Keywords

fuzzy cluster analysis, incomplete data, cluster tendency, cluster validity

Abstract

The quality of results for partitioning clustering algorithms depends on the assumption made on the number of clusters presented in the data set. Applying clustering methods on real data missing values turn out to be an additional challenging problem for clustering algorithms. Fuzzy clustering approaches adapted to incomplete data perform well for a given number of clusters. In this study, we analyse different cluster validity functions in terms of applicability on incomplete data on the one hand. On the other hand we analyse in experiments on several data sets to what extent the clustering results produced by fuzzy clustering methods for incomplete data reflect the distribution structure of data.

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/).

Download article (PDF)

TY  - CONF
AU  - Ludmila Himmelspach
AU  - Daniel Hommers
AU  - Stefan Conrad
PY  - 2011/08
DA  - 2011/08
TI  - Cluster Tendency Assessment for Fuzzy Clustering of Incomplete Data
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)
PB  - Atlantis Press
SP  - 290
EP  - 297
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.136
DO  - 10.2991/eusflat.2011.136
ID  - Himmelspach2011/08
ER  -