No abstract

Here the problem of selecting the number of clusters in cluster analysis is considered. Recently, the concept of clustering stability, which measures the robustness of any given clustering algorithm, has been utilized in Wang (2010) for selecting the number of clusters through cross validation. In this manuscript, an estimation scheme for clustering instability is developed based on the bootstrap, and then the number of clusters is selected so that the corresponding estimated clustering instability is minimized. The proposed selection criterion's effectiveness is demonstrated on simulations and real examples.

No abstract

SummaryLarge margin classifiers have proven to be effective in delivering high predictive accuracy, particularly those focusing on the decision boundaries and bypassing the requirement of estimating the class probability given input for discrimination. As a result, these classifiers may not directly yield an estimated class probability, which is of interest itself. To overcome this difficulty, this article proposes a novel method to estimate the class probability through sequential classifications, by utilising features of interval estimation of large margin classifiers.The method uses sequential classifications to bracket the class probability to yield an estimate up to the desired level of accuracy. The method is implemented for support vector machines and ψ-learning, in addition to an estimated Kullback-Leibler loss for tuning. A solution path of the method is derived for support vector machines to further reduce its computational cost. Theoretical and numerical analyses indicate that the method is highly competitive against alternatives, especially when the dimension of input greatly exceeds the sample size.Finally, an application to leukaemia data is described.

No abstract