Speeding up k-means by approximating Euclidean distances via block vectors
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Abstract
This paper introduces a new method to approximate Euclidean distances between points using block vectors in combination with the H¨older inequality. By defining lower bounds based on the proposed approximation, cluster algorithms can be considerably accelerated without loss of quality. In extensive experiments, we show a considerable reduction in terms of computational time in comparison to standard methods and the recently proposed Yinyang k-means. Additionally we show that the memory consumption of the presented clustering algorithm does not depend on the number of clusters, which makes the approach suitable for large scale problems.
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