Abstract
This paper presents a new process for modular clustering of complex data, such as that used in remote sensing images. This method performs feature weighting in a wrapper approach. The proposed method combines several local specialists, each one extracting one cluster only and using different feature weights. A new clustering quality criterion, adapted to independant clusters, is defined. The weight learning is performed through a cooperative coevolution algorithm, where each species represents one of the clusters to be extracted.
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Blansché, A., Gançarski, P., Korczak, J.J. (2005). A Coevolutionary Approach for Clustering with Feature Weighting Application to Image Analysis. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_26
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DOI: https://doi.org/10.1007/978-3-540-32003-6_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25396-9
Online ISBN: 978-3-540-32003-6
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