Spatial associative classification: propositional vs structural approach

Author:

Ceci Michelangelo,Appice Annalisa

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Reference47 articles.

1. Aiello, M. (2001). Spatial reasoning: theory and practice. PhD thesis, University of Amsterdam, Holland.

2. Appice, A., Ceci, M., Lanza, A., Lisi, F. A., & Malerba, D. (2003). Discovery of spatial association rules in georeferenced census data: A relational mining approach. Intelligent Data Analysis, 7(6), 541–566.

3. Appice, A., Ceci, M., Rawles, S., & Flach, P. A. (2004a). Redundant feature elimination for multi-class problems. In Greiner, R. & Schuurmans, D. (Eds.), Proceedings of the 21st international conference on machine learning, (pp. 33–40). New York: ACM.

4. Appice, A., Lanza, A., Malerba, D., & Turi, A. (2004b). Mining spatial association rules from census data with ARES. In May, M. & Malerba, D. (Eds.), Notes of the KdNet workshop symposium knowledge-based services for public sector: mining official data.

5. Baralis, E., & Garza, P. (2003). Majority classification by means of association rules. In Lavrac, N., Gamberger, D., Todorovski, L., & Blockeel, H. (Eds.), Proceedings of the 7th European conference on principles and practice of knowledge discovery in databases, volume 2838 of LNAI. (pp. 35–46). Berlin Heidelberg New York: Springer.

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