Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
Model-based evaluation of clustering validation measures
Pattern Recognition
An effective evaluation measure for clustering on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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The concept of cluster stability is introduced to assess the validity of data partitionings found by clustering algorithms. It allows us to explicitly quantify the quality of a clustering solution, without being dependent on external information. The principle of maximizing the cluster stability can be interpreted as choosing the most self-consistent data partitioning. We present an empirical estimator for the theoretically derived stability index, based on resampling. Experiments are conducted on well known gene expression data sets, reanalyzing the work by Alon et al. [1] and by Spellman et al. [8].