A wrapper that applies a black-box algorithm to a sample and tests clustering quality over $X, adaptively increasing the sample size is proposed, ...

Edith Cohen, Shiri Chechik, Haim Kaplan: Clustering over Multi-Objective Samples: The one2all Sample. CoRR abs/1706.03607 (2017). manage site settings.

Abstract. Clustering of data points is a fundamental tool in data analysis. We consider points X in a relaxed metric space, where the.

Our wrapper uses the smallest sample that provides statistical guarantees that the quality of the clustering on the sample carries over to the full data set. We ...

Apr 29, 2018 · For cost estimation, we apply one2all with a bicriteria approximate M, while adaptively balancing |M| and α to optimize sample size per quality.

At the core of our design is the {\em one2all} construction of multi-objective probability-proportional-to-size (pps) samples: Given a set M of centroids ...

Multi-objective samples build on the classic notion of sample coordination ... Clustering over multi-objective samples: The one2all sample. CoRR, abs ...

The core of the design are the novel one2all probabilities, computed for a set M of centroids and α ≥ 1: the clustering cost of each Q with cost V(Q) ...

For cost estimation, we apply one2all with a bicriteria approximate M, while adaptively balancing |M| and α to optimize sample size per quality. For clustering, ...

At the core of our design is the {\em one2all} construction of multi-objective probability-proportional-to-size (pps) samples: Given a set M of centroids ...