Abstract
Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. Aggregating these to a "common" solution amounts to finding a consensus clustering, which can be characterized in a general optimization framework. We discuss recent conceptual and computational advances in this area, and indicate how these can be used for analyzing the structure in cluster ensembles by clustering its elements. © Springer-Verlag Berlin, Heidelberg 2005.
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CITATION STYLE
Hornik, K. (2005). Cluster ensembles. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 65–72). Kluwer Academic Publishers. https://doi.org/10.1007/3-540-28084-7_6
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