QUAC: Quick unsupervised anisotropic clustering

We present a novel unsupervised algorithm for quickly finding clusters in multidimensional data. It does not make the assumption of isotropy, instead taking full advantage of the anisotropic Gaussian kernel, to adapt to local data shape and scale. We employ some little-used properties of the multivariate Gaussian distribution to represent the data, and also give, as a corollary of the theory we formulate, a simple yet principled means of preventing singularities in Gaussian models. The efficacy and robustness of the proposed method is demonstrated on both real and artificial data, providing qualitative and quantitative results, and comparing against the well known mean-shift and K-means algorithms.