Learning topological constraints in self-organizing map

Authors:
Guénaël Cabanes;Younès Bennani
Affiliations:
LIPN, CNRS, UMR, Université de Paris 13, Villetaneuse, France;LIPN, CNRS, UMR, Université de Paris 13, Villetaneuse, France
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

The Self-Organizing Map (SOM) is a popular algorithm to analyze the structure of a dataset. However, some topological constraints of the SOM are fixed before the learning and may not be relevant regarding to the data structure. In this paper we propose to improve the SOM performance with a new algorithm which learn the topological constraints of the map using data structure information. Experiments on artificial and real databases show that algorithm achieve better results than SOM. This is not the case with trivial topological constraint relaxation because of the high increase of the Topological error.