Ensemble approach has been successfully applied in the context of supervised learning to increase the accuracy and stability of classification. One of the most popular method is bagging based on bootstrap samples. Recently, analogous techniques for cluster analysis have been suggested in order to increase classification accuracy, robustness and stability of the clustering solutions. Research has proved that, by combining a collection of different clusterings, an improved solution can be obtained. A desirable quality of the method is the stability of a clustering algorithm with respect to small perturbations of data (e.g., data subsampling or resampling, small variations in the feature values) or the parameters of the algorithm (e.g., random initialization). Here, we look at the stability of the ensemble and carry out an experimental study to compare stability of cluster ensembles based on bagging idea.
These paper considers a risk model for two dependant classes of insurance business. The dependence between these classes is caused by appearing of some claims at the same time in both classes and additionally the sizes of these claims are dependant. The structure of the dependence between these claims sizes is described by copulas. The main aim of the paper is to investigate the impact of the level of dependence between these claims sizes on the finite-time ruin probability in considered risk model. short numerical analysis.
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