Please use this identifier to cite or link to this item: https://doi.org/10.1007/BF02626093
Title: Constructing optimal ultrametrics
Authors: Sriram, N. 
Lewis, S.
Keywords: Clustering
Graph
Heuristic
Hierarchical
Simulation
Issue Date: Dec-1993
Source: Sriram, N., Lewis, S. (1993-12). Constructing optimal ultrametrics. Journal of Classification 10 (2) : 241-268. ScholarBank@NUS Repository. https://doi.org/10.1007/BF02626093
Abstract: Clique optimization (CLOPT) is a family of graph clustering procedures that construct parsimonious ultrametrics by executing a sequence of divisive and agglomerative operations. Every CLOPT procedure is associated with a distinct graph-partitioning heuristic. Seven HCS methods, a mathematical programming algorithm, and two CLOPT heuristics were evaluated on simulated data. These data were obtained by distorting ultrametric partitions and hierarchies. In general, internally optimal models yielded externally optimal models. By recovering near-optimal solutions more consistently, CLOPT2 emerged as the most robust technique. © 1993 Springer-Verlag New York Inc.
Source Title: Journal of Classification
URI: http://scholarbank.nus.edu.sg/handle/10635/50234
ISSN: 01764268
DOI: 10.1007/BF02626093
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