Stream data mining is one of the common data mining methods in real-world applications nowadays. However, it is challenging due to the nature of data stream in real-world, especially concept drift. To handle concept drift, drift detection method is necessary when the accessing data label is unavailable. In this paper, we propose a drift detection method based on the statistical test with clustering as preprocessing and reduce the execution time with principal component analysis (PCA) for the feature extraction method. Experiment result on synthetic and real-world streaming data show the clustering preprocessing improve the performance of the drift detection and feature extraction trade-off an insignificant performance of detection for great speed up for the execution time.