Abstract
This paper proposes a method for empirical comparison of distances for agglomerative hierarchical clustering based on rough set-based approximation. When a set of target is given, a level of clustering tree where one branch includes all the targets can be traced with the number of elements included. The pair \((\#clusters of a level, \#elements of a cluster) \) can be viewed as indices-pair for a given clustering tree.
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This research is supported by Grant-in-Aid for Scientific Research (B) 15H2750 from Japan Society for the Promotion of Science(JSPS).
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Tsumoto, S., Kimura, T., Iwata, H., Hirano, S. (2018). Empirical Comparison of Distances for Agglomerative Hierarchical Clustering. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-91476-3_45
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