Extracting spatial semantics in association rules for ocean image retrieval

Authors:
Li-Jen Kao;Yo-Ping Huang;Frode Eika Sandnes
Affiliations:
Department of Multimedia and Game Science, Taipei College of Maritime Technology, Taipei, Taiwan;Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan;Faculty of Engineering, Oslo University College, Oslo, Norway
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

Citing 7
Cited 0

Mining quantitative association rules in large relational tables

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Efficient Mining of Intertransaction Association Rules

IEEE Transactions on Knowledge and Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth

Proceedings of the 17th International Conference on Data Engineering
Discovering Spatial Co-location Patterns: A Summary of Results

SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Discovering Colocation Patterns from Spatial Data Sets: A General Approach

IEEE Transactions on Knowledge and Data Engineering
Building image mosaics: an application of content-based image retrieval

ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Efficient mining of salinity and temperature association rules from ARGO data

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Evolutionary multiobjective optimization (EMO) is an active research area in the field of evolutionary computation. EMO algorithms are designed to find a non-dominated solution set that approximates the entire Pareto front of a multiobjective optimization ...