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Clustering algorithms are important methods widely used in mining data streams because of their abilities to deal with infinite data flows. Although these algorithms perform well to mining latent relationship in data streams, most of them suffer from loss of cluster purity and become unstable when the inputting data streams have too many noisy variables. In this article, we propose a clustering algorithm to cluster data streams with noisy variables. The result from simulation shows that our proposal method is better than previous studies by adding a process of variable selection as a component in clustering algorithms. The results of two experiments indicate that clustering data streams with the process of variable selection are more stable and have better purity than those without such process. Another experiment testing KDD-CUP99 dataset also shows that our algorithm can generate more stable result. [ABSTRACT FROM AUTHOR]

In this article, we introduce the L1 regularized support vector machine (L1-SVM) as an effective feature selection technique into the modeling of financial early warning system (EWS), for the purpose of establishing compact financial EWSs for Chinese-listed companies. By introducing LASSO penalty into the SVM framework, the L1-SVM is a capable methodology to select causative features in classification problem. We evaluate the feature selection performance of L1-SVM under different circumstances through numerical simulations and find it suitable for selecting features for financial EWS. In the real study, we establish four financial EWSs with features selected by L1-SVM and compare them with those trained with full features. The empirical result illustrates that our EWSs, with only minority of features, outperform significantly than full ones in the respect of generalization performance, which indicates the feasibility of L1-SVM in real applications. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]