A Monte Carlo algorithm for fast projective clustering

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Matrix approximation and projective clustering via volume sampling (2006, methodology): “Algorithms and systems based on projective clustering have been applied to facial recognition, datamining, and synthetic data [5, 29, 6], motivated by the observation that no single subspace performs as well as a few different subspaces.”
Subspace clustering (2012, background): “This observation motivates research on adaptive density thresholds (e.g., see Refs 46 and 92).”
Automatic Subspace Clustering of High Dimensional Data (2005, neutral): “Recently ( Procopiuc et al .”
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data (2007, background): “The first type is to find out the exact subspaces of different clusters (see, for instance, [6], [7], [8], [9], [10], [11], [12]).”
Clustering ensembles: models of consensus and weak partitions (2005, methodology): “For example, algorithms such as CLIQUE [2] and DOC [42] can discover both useful projections as well as data clusters.”
Subspace clustering for high dimensional data: a review (2004, background): “DOC proposes a mathematical definition for the notion of an optimal projective cluster [64].”
Evaluating Clustering in Subspace Projections of High Dimensional Data (2009, methodology): “More recent approaches like DOC use flexible hypercubes of width w [29].”