As there are more and more surveillance cameras installed in public places, a challenging problem is to discover unusual behavior patterns from a huge amount of video data. However, this task is currently only feasible for human beings because both object recognition and intention detection are still difficult for computer vision. Recently, the development of low-cost depth cameras significantly improves the efficiency and effectiveness of capturing motion data. We thus propose in this work an algorithmic scheme that extracts unusual behavior patterns from motion capture data. Specifically, feature extraction and data clustering techniques are applied in our scheme so as to detect such outlier patterns. Example applications of our scheme include public area surveillance and home healthcare.