---
title: "A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns"
authors:
  - "Michèle Sebag"
  - "Nicolas Tarrisson"
  - "Olivier Teytaud"
  - "Julien Lefèvre"
  - "Sylvain Baillet"
doi: ""
url: "https://ijcai.org/proceedings/2005/127"
pdf: "https://ijcai.org/proceedings/2005/0127.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "859-863"
track: ""
keywords:
---

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

Michèle Sebag, Nicolas Tarrisson, Olivier Teytaud, Julien Lefèvre, Sylvain Baillet

*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.