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Research map / Learning theory and algorithms / Clustering

Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut

NeurIPS 2020 · Learning theory and algorithms › Clustering · approach: Correlation clustering · 14 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use k-sums (kNN-graph distance sum, linear time) instead of k-meansclustering · Face-94clustering accuracy: 0.722 -> 0.961Helped
Use k-sums instead of k-meansclustering · Olivetticlustering accuracy: 0.462 -> 0.367Hurt
Use k-sums instead of k-meansclustering · Fingerclustering accuracy: 0.311 -> 0.395Mixed
Use k-sums instead of k-meansclustering · Grid-100wrunning time (s): 3416.70 -> 221.50Helped
Use k-sums instead of k-meansclustering · CelebArunning time (s): 2.3E2 -> 31.2Helped
Use k-sums instead of k-meansclustering · CelebAF1 score: 0.347 -> 0.464Helped
Use k-sums instead of k-meansclustering · CPLFWF1 score: 0.477 -> 0.404Hurt
Use k-sums instead of k-meansclustering · AdienceF1 score: 0.614 -> 0.572Hurt
Use k-sums instead of k-meansclustering · PEALF1 score: 0.785 -> 0.871Helped

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