Without any prior information about data distribution, parameter and the labels of data, not all base clustering results can truly benefit for the combination decision of clustering ensemble. In addition, if each base clustering plays the same role, the performance of clustering ensemble may be weakened. This paper proposed a selective K-means clustering ensemble based on random sampling, called RS-KMCE. In RS-MKCE, random sampling can avoid local minimum in the process of selecting base clustering subset for ensemble. And the defined evaluation index according to diversity and accuracy can lead to a better base clustering subset for improving the performance of clustering ensemble. The experiment results on two synthetic datasets and four UCI datasets show that performance of the proposed RS-KMCE is better than K-means, K-means clustering ensemble, and selective K-means clustering ensemble based on bagging.

To improve the calculation speed and robustness of the Speaker Recognition (SR) system, the authors proposed a speaker recognition algorithm method based on utterance level Principal Component Analysis (PCA), which was derived from the frame level features. Instead of frame level features, this algorithm used the utterance level features in both training and recognition. What's more, the PCA method was also used for dimension reduction and redundancy removing. The experimental results show that this algorithm not only gets a little higher recognition rate, but also suppresses the effect of the noise on speaker recognition system. It verifies that the algorithm based on utterance level features PCA can get faster recognition speed and higher system recognition rate, and it enhances system recognition rate in different noise environments under different Signal-to-Noise Ratio (SNR) conditions.