Resource-bounded reasoning in intelligent systems
ACM Computing Surveys (CSUR) - Special issue: position statements on strategic directions in computing research
Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Translation-invariant mixture models for curve clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
CircleView: a new approach for visualizing time-related multidimensional data sets
Proceedings of the working conference on Advanced visual interfaces
Multi-objective rule mining using genetic algorithms
Information Sciences: an International Journal - Special issue: Soft computing data mining
Interval query indexing for efficient stream processing
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Functional brain imaging with multi-objective multi-modal evolutionary optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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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.