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The (black) art of runtime evaluation: Are we comparing algorithms or implementations?

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Abstract

Any paper proposing a new algorithm should come with an evaluation of efficiency and scalability (particularly when we are designing methods for “big data”). However, there are several (more or less serious) pitfalls in such evaluations. We would like to point the attention of the community to these pitfalls. We substantiate our points with extensive experiments, using clustering and outlier detection methods with and without index acceleration. We discuss what we can learn from evaluations, whether experiments are properly designed, and what kind of conclusions we should avoid. We close with some general recommendations but maintain that the design of fair and conclusive experiments will always remain a challenge for researchers and an integral part of the scientific endeavor.

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Notes

  1. By the Twitter terms of use, we are not allowed to share this data set, unfortunately.

  2. Hartigan’s more general model also laid the ground for density-based clustering, see the discussion by Campello et al. [26].

  3. Müllner [56] further refines this idea, but we do not have an implementation of this.

  4. Nevertheless, the presented observations agree with theoretical complexity results.

  5. This issue is resolved in the next version of Shogun, after our bug report.

  6. Available at http://elki.dbs.ifi.lmu.de/wiki/DataSets/MultiView.

  7. https://github.com/alitouka/spark_dbscan.

  8. Reported as: https://github.com/JuliaStats/Clustering.jl/issues/58.

  9. Also some of the involved authors have a high reputation. Our selection is not intended to harm their reputation but to put the attention of the community to these aspects of runtime evaluation. The fact that these papers by world class authors have been accepted at world class venues only shows that neither the authors nor the reviewers were sufficiently aware of the pitfalls of runtime analysis and of the limits on the conclusions such experiments support.

  10. We experimentally measured a 500 times slower execution when using Euclidean distance implemented in the Python interpreter as opposed to the provided Euclidean distance which is compiled from Cython. There currently is no way around this overhead without integrating a just-in-time compiler: https://github.com/scikit-learn/scikit-learn/issues/6256.

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Authors and Affiliations

  1. Institute for Informatics, Ludwig-Maximilians-Universität München, Oettingenstr. 67, 80538, Munich, Germany

    Hans-Peter Kriegel & Erich Schubert

  2. Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark

    Arthur Zimek

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  1. Hans-Peter Kriegel
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  3. Arthur Zimek
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Correspondence to Arthur Zimek.

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Kriegel, HP., Schubert, E. & Zimek, A. The (black) art of runtime evaluation: Are we comparing algorithms or implementations?. Knowl Inf Syst 52, 341–378 (2017). https://doi.org/10.1007/s10115-016-1004-2

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