Cluster Tendency Assessment for Fuzzy Clustering of Incomplete Data
Ludmila Himmelspach, Daniel Hommers, Stefan Conrad
Available Online August 2011.
- https://doi.org/10.2991/eusflat.2011.136How to use a DOI?
- fuzzy cluster analysis, incomplete data, cluster tendency, cluster validity
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 290 EP - 297 SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2011.136 DO - https://doi.org/10.2991/eusflat.2011.136 ID - Himmelspach2011/08 ER -