Abstract
Indexed hierarchies and indexed clustering pyramids are based on an underlying order for which each cluster is connected. In this paper our aim is to 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 in one-to-one correspondence with a new kind of dissimilarities. We give a building algorithm for convex spatial pyramids illustrated by an example. We show that spatial pyramids can converge towards geometrical pyramids. We indicate finally that spatial pyramids can give better results than Kohonen mappings and can produce a geometrical representation of conceptual lattices.
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Diday, E. (2004). Spatial Pyramidal Clustering Based on a Tessellation. In: Banks, D., McMorris, F.R., Arabie, P., Gaul, W. (eds) Classification, Clustering, and Data Mining Applications. Studies in Classification, Data Analysis, and Knowledge Organisation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17103-1_12
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DOI: https://doi.org/10.1007/978-3-642-17103-1_12
Publisher Name: Springer, Berlin, Heidelberg
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