Abstract
Although learning vector quantization (LVQ) based on learning concept is a typical clustering method, we cannot necessarily obtain satisfactory classification results for linearly separable data. In this paper, a new clustering method based on LVQ and a split and merge procedure is proposed to realize reliable classification. Introducing a criterion of whether or not there is only one cluster in each class after clustering by LVQ, split subclasses in a class are merged into appropriate neighboring classes except one subclass. And the validity of the classification result is checked. Under several classification experiments, the performance of the proposed method is provided.
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Morii, F. (2008). Clustering Based on LVQ and a Split and Merge Procedure. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_7
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DOI: https://doi.org/10.1007/978-3-540-69162-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
Online ISBN: 978-3-540-69162-4
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