A multi-objective multi-modal optimization approach for mining stable spatio-temporal patterns

A multi-objective multi-modal optimization approach for mining stable spatio-temporal patterns (2005)

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by Michèle Sebag , Nicolas Tarrisson , Olivier Teytaud , Julien Lefevre , Sylvain Baillet , La Pitié Salpétrière , F- Paris

Venue:	In Proc. of Int. Joint Conf. on AI, IJCAI’05
Citations:	2 - 2 self

BibTeX

@INPROCEEDINGS{Sebag05amulti-objective,
    author = {Michèle Sebag and Nicolas Tarrisson and Olivier Teytaud and Julien Lefevre and Sylvain Baillet and La Pitié Salpétrière and F- Paris},
    title = {A multi-objective multi-modal optimization approach for mining stable spatio-temporal patterns},
    booktitle = {In Proc. of Int. Joint Conf. on AI, IJCAI’05},
    year = {2005},
    pages = {859--864}
}

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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. 1

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