Fast K-means-like clustering in metric spaces

Abstract A fast K -means-like algorithm is proposed for the case in which data cannot be adequately represented in a suitable vector space, though a metric is available to measure the dissimilarity between data points. Experimental results comparing the proposed algorithm with a direct K -means-like method show a substantial reduction of the cost in terms of distance computations required.

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