Abstract
In this study, a new approach to Kohonen Self-Organizing Maps fusion is presented: the use of modified cluster validity indexes as a criterion for merging Kohonen Maps. Computational simulations were performed with traditional dataset from the UCI Machine Learning Repository, with variations in map size, number of subsets to be merged and the percentage of dataset bagging. The fusion results were compared with a regular single Kohonen Map. In some selected parameters, the proposed method achieves a better accuracy measure. © 2014 Springer International Publishing.
Author supplied keywords
Cite
CITATION STYLE
Pasa, L. A., Costa, J. A. F., & De Medeiros, M. G. (2014). Fusion of Kohonen Maps ranked by cluster validity indexes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8480 LNAI, pp. 654–665). Springer Verlag. https://doi.org/10.1007/978-3-319-07617-1_57
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
