Proceedings

A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns

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Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 859-863.

Abstract

This paper, motivated by functional brain imaging applications, is interested in the discovery of stable spatio-temporal patterns. This problem is formalized as a multi-objective multi-modal optimization problem: on one hand, the target patterns must show a good stability in a wide spatio-temporal region (antagonistic objectives); on the other hand, experts are interested in finding all such patterns (global and local optima). The proposed algorithm, termed 4D-Miner, is empirically validated on artificial and real-world datasets; it shows good performances and scalability, detecting target spatiotemporal patterns within minutes from 400+ Mo datasets.

Michèle Sebag, Nicolas Tarrisson, Olivier Teytaud, Julien Lefèvre, and Sylvain Baillet. 2005. A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05. IJCAI, 859–863. https://ijcai.org/proceedings/2005/127
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