Coresets are among the most popular paradigms for summarizing data. In particular, there exist many high performance coresets for clustering problems such as k-means in both theory and practice. Curiously, there exists no work on comparing the quality of available k-means coresets. In this paper we perform such an evaluation. There currently is no algorithm known to measure the distortion of a candidate coreset. We provide some evidence as to why this might be computationally difficult. To complement this, we propose a benchmark for which we argue that computing coresets is challenging and which also allows us an easy (heuristic) evaluation of coresets. Using this benchmark and real-world data sets, we conduct an exhaustive evaluation of the most commonly used coreset algorithms from theory and practice.
@InProceedings{schwiegelshohn_et_al:LIPIcs.ESA.2022.84, author = {Schwiegelshohn, Chris and Sheikh-Omar, Omar Ali}, title = {{An Empirical Evaluation of k-Means Coresets}}, booktitle = {30th Annual European Symposium on Algorithms (ESA 2022)}, pages = {84:1--84:17}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-247-1}, ISSN = {1868-8969}, year = {2022}, volume = {244}, editor = {Chechik, Shiri and Navarro, Gonzalo and Rotenberg, Eva and Herman, Grzegorz}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2022.84}, URN = {urn:nbn:de:0030-drops-170225}, doi = {10.4230/LIPIcs.ESA.2022.84}, annote = {Keywords: coresets, k-means coresets, evaluation, benchmark} }
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