Abstract
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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Lu, X., Yang, Y., Wang, H. (2013). Selective Clustering Ensemble Based on Covariance. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_16
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DOI: https://doi.org/10.1007/978-3-642-38067-9_16
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