Abstract
An encoding method has a direct effect on the quality and the representation of the discovered knowledge in data mining systems. Biological macromolecules are encoded by strings of characters, calledprimary structures. Knowing that data mining systems usually use relational tables to encode data, we have then to reencode these strings and transform them into relational tables. In this paper, we do a comparative study of the existingstatic encoding methods, that are based on the Biologist know-how, and our newdynamic encoding one, that is based on the, construction ofDiscriminant and Minimal Substrings (DMS). Different classification methods are used to do this study. The experimental results show that ourdynamic encoding method is more efficient than thestatic ones, to encode biological macromolecules within a data mining perspective.
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Mondher Maddouri received an B.S. degree in mathematics and physics in 1990, an M.S. degree in computer engineering in 1994 and a Ph.D. degree in computer science in 2000, from the Faculty of Sciences of Tunis, Tunisia. He is currently an associate professor in the Computer Science Department in the National Institute of Applied Sciences and Technologies, Tunis, Tunisia. His research interests are machine learning, knowledge discovery and data mining, and computational molecular biology.
Mourad Elloumi received an B.S. degree in mathematics and physics in 1984, and an M.S. degree in computer engineering in 1988, from the Faculty of Sciences of Tunis, Tunisia. He also received an M.S. degree in computer science in 1989, and a Ph.D. degree in computer science in 1994, from the University of Aix-Marseilles III, France. He is currently an associate professor in the Computer Science Department in the Faculty of Economic Sciences and Management of Tunis, Tunisia. His research interests are computational molecular biology, algorithmics, and knowledge discovery and data mining.
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Maddouri, M., Elloumi, M. Encoding of primary structures of biological macromolecules within a data mining perspective. J. Comput. Sci. & Technol. 19, 78–88 (2004). https://doi.org/10.1007/BF02944786
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DOI: https://doi.org/10.1007/BF02944786