Generalized derivative based kernelized learning vector quantization
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
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In this paper we develop a Tanimoto metric variant of the Evolving Tree for the analysis of mass spectrometric data of animal fur. The Evolving Tree is an extension of Self-Organizing Maps developed to analyze hierarchical clustering problems. Together with the Tanimoto similarity measure, which is intended to work with taxonomic structured data, the Evolving Tree is well suited for the identification of animal hair based on mass spectrometry fingerprints. Results show a suitable hierarchical clustering of the test data and also a good retrieval capability with a logarithmic number of comparisons.