Menendez, HD;
(2019)
VARMOG: A Co-Evolutionary Algorithm to Identify Manifolds on Large Data.
In:
Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC).
(pp. pp. 3300-3307).
IEEE
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Abstract
Detecting clusters defining a specific shape or manifold is an open problem and has, indeed, inspired different machine learning algorithms. These methodologies normally lack scalability, as they depend on the performance of very sophisticated processes, such as extracting the Laplacian of a similarity graph in spectral clustering. When the algorithms need not only to identify manifolds on large amounts of data or streams, but also select the number of clusters, they failed either because of the robustness of their processes or by computational limitations. This paper introduces a general methodology that works in two levels: the initial step summarizes the data into a set of relevant features using the Euclidean properties of manifolds, and the second applies a robust methodology based on a co-evolutionary multi-objective clustering algorithm that identifies both, the number of manifolds and their associated manifold. The results show that this method outperforms different state of the art clustering processes for both, benchmark and real-world datasets.
| Type: | Proceedings paper |
|---|---|
| Title: | VARMOG: A Co-Evolutionary Algorithm to Identify Manifolds on Large Data |
| Event: | 2019 IEEE Congress on Evolutionary Computation (CEC) |
| Location: | Wellington (NZ), New Zealand |
| Dates: | 10th-13th June 2019 |
| ISBN-13: | 978-1-7281-2153-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CEC.2019.8790004 |
| Publisher version: | https://doi.org/10.1109/CEC.2019.8790004 |
| 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 algorithms, Manifolds, Sociology, Statistics, Biological cells, Genetics, Machine learning algorithms |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10082908 |
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