Titlebook: Classification, Clustering, and Data Mining Applications; Proceedings of the M David Banks,Frederick R. McMorris,Wolfgang Gaul Conference p

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Catherine Adams,Terrie Lynn Thompson space. Ultrametric distance is defined from p-adic valuation. It is known that ultrametricity is a natural property of spaces that are sparse. Here we look at the quantification of ultrametricity. We also look at data compression based on a new ultrametric wavelet transform. We conclude with comput

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https://doi.org/10.1007/978-981-10-8980-0y with the formation of clusters. The method treats only one variable at each stage; a single variable is chosen to split a cluster into two sub-clusters. Then the sub-clusters are successively split until a stopping criterion is satisfied. The original splitting criterion is the solution of a maxim

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https://doi.org/10.1007/978-3-030-87710-1e CAHCVR algorithm (Classification Ascendante Hiérarchique sous Contrainte de contiguïté et par agrégation des Voisins Réciproques). The results are compared to those obtained with the inertia criterion (Ward) in the context of digital image segmentation. New strategies concerning multiple aggregati

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Computer-Mediated Peer Response,extend standard pyramids and their underlying one-to-one correspondence with Robinsonian dissimilarities to spatial pyramids where each cluster is “compatible” with a spatial network given by a kind of tessellation called “m/k-network”. We focus on convex spatial pyramids and we show that they are i

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