Selective Clustering Ensemble Based on Covariance

Clustering Ensemble effectively improves clustering accuracy, stability and robustness, which is most resulted from the diversity of the base clustering results. It is a key point to measure the diversity of clustering results. This paper proposes a method to measure diversity of base clustering results and a covariance-based selective clustering ensemble algorithm. Experiments on 20 UCI data sets show that this algorithm effectively improves the clustering performance.

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