Monotone invariant clustering procedures |
| |
Authors: | Lawrence Hubert |
| |
Institution: | (1) The University of Wisconsin, USA |
| |
Abstract: | A major justification for the hierarchical clustering methods proposed by Johnson is based upon their invariance with respect
to monotone increasing transformations of the original similarity measures. Several alternative procedures are presented in
this paper that also share in the same property of invariance. One of these techniques constructs a hierarchy of partitions
by sequentially minimizing a monotone invariant goodness-of-fit statistic; the other techniques construct a hierarchy of partitions
by successively subdividing the complete set of objects until one partition class is defined for each individual member in
the set. A numerical example comparing these alternative procedures with Johnson's two methods is duscussed in terms of a
simplified computational scheme for obtaining the necessary hierarchies. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |