  We present a new framework to detect various types of variable objects within
massive astronomical time-series data. Assuming that the dominant population of
objects is non-variable, we find outliers from this population by using a
non-parametric Bayesian clustering algorithm based on an infinite
GaussianMixtureModel (GMM) and the Dirichlet Process. The algorithm extracts
information from a given dataset, which is described by six variability
indices. The GMM uses those variability indices to recover clusters that are
described by six-dimensional multivariate Gaussian distributions, allowing our
approach to consider the sampling pattern of time-series data, systematic
biases, the number of data points for each light curve, and photometric
quality. Using the Northern Sky Variability Survey data, we test our approach
and prove that the infinite GMM is useful at detecting variable objects, while
providing statistical inference estimation that suppresses false detection. The
proposed approach will be effective in the exploration of future surveys such
as GAIA, Pan-Starrs, and LSST, which will produce massive time-series data.
