Abstract
Algorithms of agglomerative hierarchical clustering using asymmetric similarity measures are studied. Two different measures between two clusters are proposed, one of which generalizes the average linkage for symmetric similarity measures. Asymmetric dendrogram representation is considered after foregoing studies. It is proved that the proposed linkage methods for asymmetric measures have no reversals in the dendrograms. Examples based on real data show how the methods work.
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Takumi, S., Miyamoto, S. (2011). Agglomerative Clustering Using Asymmetric Similarities. In: Torra, V., Narakawa, Y., Yin, J., Long, J. (eds) Modeling Decision for Artificial Intelligence. MDAI 2011. Lecture Notes in Computer Science(), vol 6820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22589-5_12
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DOI: https://doi.org/10.1007/978-3-642-22589-5_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22588-8
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