Shand, C;
Allmendinger, R;
Handl, J;
Webb, A;
Keane, J;
(2021)
HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis.
IEEE Transactions on Evolutionary Computation
10.1109/TEVC.2021.3137369.
(In press).
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Abstract
Comprehensive benchmarking of clustering algorithms is rendered difficult by two key factors: (i) the elusiveness of a unique mathematical definition of this unsupervised learning approach and (ii) dependencies between the generating models or clustering criteria adopted by some clustering algorithms and indices for internal cluster validation. Consequently, there is no consensus regarding the best practice for rigorous benchmarking, and whether this is possible at all outside the context of a given application. Here, we argue that synthetic datasets must continue to play an important role in the evaluation of clustering algorithms, but that this necessitates constructing benchmarks that appropriately cover the diverse set of properties that impact clustering algorithm performance. Through our framework, HAWKS, we demonstrate the important role evolutionary algorithms play to support flexible generation of such benchmarks, allowing simple modification and extension. We illustrate two possible uses of our framework: (i) the evolution of benchmark data consistent with a set of hand-derived properties and (ii) the generation of datasets that tease out performance differences between a given pair of algorithms. Our work has implications for the design of clustering benchmarks that sufficiently challenge a broad range of algorithms, and for furthering insight into the strengths and weaknesses of specific approaches.
| Type: | Article |
|---|---|
| Title: | HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TEVC.2021.3137369 |
| Publisher version: | https://doi.org/10.1109/TEVC.2021.3137369 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Clustering, evolutionary computation, synthetic data, benchmarking, data generator |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10142230 |
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