Mouse dynamics has recently become an interesting new topic in the area of behavioral biometrics due to its non-intrusiveness and convenience. Some promising results have been shown by previous researches on identity authentication and monitoring using characteristics in users' mouse actions. This paper explores mouse dynamics further by focusing on an important issue not addressed previously: behavioral variability. With an empirical study of long term behaviors of 10 computer users, we show variations are obvious in mouse activities and can have a serious impact if not considered carefully. To tackle the problem of variability, we propose a dimensionality reduction based approach which is demonstrated to be effective in our experiments. More specifically, the classification results after preprocessing by PCA and ISOMAP are shown to be much better than direct classification. Moreover, the results of a false acceptance rate (FAR) 0.55% and false rejection rate (FRR) 3.00% by the nonlinear method ISOMAP are comparable to the best result reported in literature while being subject to more behavioral variability.