Abstract
This paper is concerned with the automated induction of prototypes to represent a database in a way that combines transparency and accuracy. A clustering algorithm will be described which learns fuzzy prototypes from a set of data. The potential of the resulting method will be illustrated by its application to classification problems and comparing its performance with that of previous approaches in the literature. © Spnnger-Verlag Berlin Heidelberg 2003.
Cite
CITATION STYLE
Rodríguez, I. G., Lawry, J., & Baldwin, J. F. (2003). An iterative fuzzy prototype induction algorithm. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2686, 286–293. https://doi.org/10.1007/3-540-44868-3_37
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