Weighted self-organizing maps: incorporating user feedback

Authors:
Andreas Nürnberger;Marcin Detyniecki
Affiliations:
University of California at Berkeley, EECS, Berkeley, CA;CNRS, Laboratoire d'Informatique de Paris 6, University of Paris 6, Paris, France
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

One interesting way of accessing collections of multimedia objects is by methods of visualization and clustering. Growing self-organizing maps provide such a solution, which adapts automatically to the underlying database. Unfortunately, the result of the clustering greatly depends on the definition of the describing features and the used similarity measure. In this paper, we present a general approach to improve the obtained clustering by incorporating user feedback (in the form of drag-and-drop) into the underlying topology of the self-organizing map.