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Subspace Clustering Using Evolvable Genome Structure

Sergio Peignier 1, 2 Christophe Rigotti 2, 1, 3 Guillaume Beslon 1, 2 
1 BEAGLE - Artificial Evolution and Computational Biology
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this paper we present an evolutionary algorithm to tackle the subspace clustering problem. Subspace clustering is recognized as more difficult than standard clustering since it requires to identify not only the clusters but also the various subspaces where the clusters hold. We propose to tackle this problem with a bio-inspired algorithm that includes many bio-like features like variable genome length and organization, functional and non-functional elements, and variation operators including chromosomal rearrangements. These features give the algorithm a large degree of freedom to achieve subspace clustering with satisfying results on a reference benchmark with respect to state of the art methods. One of the main advantages of the approach is that it needs only one subspace clustering ad-hoc parameter: the maximal number of clusters. This is a single and intuitive parameter that sets the maximal level of details of the clustering, while other algorithms require more complicated parameter space exploration. The other parameters of the algorithm are related to the evolution strategy (population size, mutation rate, ...) and for them we use a single setting that turns out to be effective on all the datasets of the benchmark.
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Contributor : Christophe Rigotti Connect in order to contact the contributor
Submitted on : Monday, September 14, 2015 - 11:38:29 PM
Last modification on : Tuesday, October 25, 2022 - 4:25:04 PM


  • HAL Id : hal-01199136, version 1


Sergio Peignier, Christophe Rigotti, Guillaume Beslon. Subspace Clustering Using Evolvable Genome Structure. Genetic and Evolutionary Computation Conference (GECCO), Jul 2015, Madrid, Spain. pp.575-582. ⟨hal-01199136⟩



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