High-dimensional data pose challenges to traditional clustering algorithms due to their inherent sparsity and data tend to cluster in different and possibly overlapping subspaces of the entire feature space. Finding such subspaces is called subspace mining. We present SCHISM, a new algorithm for mining interesting subspaces, using the notions of support and Chernoff-Hoeffding bounds. We use a v...

Currently, I am especially interested in the problem of identifying similarities between high-dimensional datasets. Very often, data may be collected by a number of sources, which may be unable to share their entire datasets for reasons like confidentiality agreements, dataset location and size, etc. If there exists some similar substructure between distinct datasets, this may be exploited. For...

§0 Introduction By far the most central and important question about laws of nature is this: Are they mere patterns in the phenomena (patterns that are in some way salient, to be sure—but still, nothing more than patterns)? Or are they something more, something that somehow governs or constrains those phenomena? Disagreement over this issue constitutes the Schism in contemporary philosophical w...