Cluster Tendency Assessment for Fuzzy Clustering of Incomplete Data

Ludmila Himmelspach, Daniel Hommers, Stefan Conrad
2011 Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)  
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
more » ... d. 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.
doi:10.2991/eusflat.2011.136 dblp:conf/eusflat/HimmelspachHC11 fatcat:o3525kusnne43fmvjmtfyf3bj4