Abstract
This article describes a technique that aims at qualifying a concept hierarchy with colors, in such a way that it can be feasible to promote the interactivity between the user and an incremental probabilistic concept formation algorithm. The main idea behind this technique is to use colors to map the concept properties being generated, to combine them, and to provide a resulting color that will represent a specific concept. The intention is to assign similar colors to similar concepts, thereby making it possible for the user to interact with the algorithm and to intervene in the concept formation process by identifying which approximate concepts are being separately formed. An operator for interactive merge has been used to allow the user to combine concepts he/she considers similar. Preliminary evaluation on concepts generated after interaction has demonstrated improved accuracy.
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References
Altintas, I.: N.:Incremental Conceptual Clustering without Order Dependency. Master’s Degree Thesis, Middle East Technical University (1995)
Biswas, G., Weiberg, J., Li, C.:ITERATE: A Conceptual clustering method for knowledge discovery in databases. In: Innovative Applications of Artificial Intelligence in the Oil and Gas Industry, Editions Technip (1994)
Fisher, D.H.: Knowledge Acquisition via Incremental Conceptual Clustering. Machine Learning 2, 139–172 (1987)
Fisher, D., Xu, L., Zard, N.: Order effects in clustering. In: Proceedings of the Ninth International Conference on Machine Learning, pp. 163–168. Morgan Kaufmann, Aberdeen (1992)
Fisher, D.: Iterative optimization and simplification of hierarchical clusterings. Journal of Artificial Intelligence and Research 4, 147–179 (1996)
Fortner, B., Meyer, T.E.: Number by Colors: A Guide to Using Color to Understand Technical Data. Springer, Heidelberg (1997) ISBN 0-387-94685-3
McKusick, K., Langley, P.: Constraints on Tree Structure in Concept Formation. In: Proceedings of the 12th International Joint Conference on Artificial Intelligence, Sydney, Australia, pp. 810–816 (1991)
Sharma, G., Trussell, H.J.: Digital Color Imaging. IEEE Transactions on Image Processing 6 (7) (1997)
Talavera, L., Béjar, J.:Efficient and Comprehensible Hierarchical Clusterings. In: Proceedings of the First Catalan Conference on Artificial Intelligence, CCIA 1998. Tarragona, Spain, ACIA Bulletin, vol. 14-15, pp. 273-281(1998)
UCI (2003), In http://www.ics.uci.edu/~mlearn/MLSummary.html/01/03/
Wyszecki, G., Stiles, W.S.: Color Science: Concepts and Methods Quantitative Data and Fornulae, 2nd edn. Wiley, New York (1982)
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Furtado, V., Cavalcante, A. (2004). Using Color to Help in the Interactive Concept Formation. In: Bazzan, A.L.C., Labidi, S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science(), vol 3171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28645-5_16
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DOI: https://doi.org/10.1007/978-3-540-28645-5_16
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