CONSTRUCTING OPTIMAL ULTRAMETRICS

被引：4
|
作者：
SRIRAM, N [1 ]
机构：
[1] AT&T BELL LABS,MURRAY HILL,NJ 07974
来源：
JOURNAL OF CLASSIFICATION | 1993年 / 10卷 / 02期
关键词：
CLUSTERING; GRAPH; HEURISTIC; HIERARCHICAL; SIMULATION;
D O I：
10.1007/BF02626093
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
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.
引用
收藏
页码：241 / 268
页数：28
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