Big data clustering via random sketching and validation

Abstract

As the number and dimensionality of data increases, development of new efficient processing tools has become a necessity. The present paper introduces a novel dimensionality reduction approach for fast and efficient clustering of high-dimensional data. The new methods extend random sampling and consensus (RANSAC) arguments, originally developed for robust… (More)
DOI: 10.1109/ACSSC.2014.7094614

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