Hierarchical Grouping to Optimize an Objective Function

1963 Journal of the American Statistical Association 18,547 citations

Abstract

Abstract A procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical. Given n sets, this procedure permits their reduction to n − 1 mutually exclusive sets by considering the union of all possible n(n − 1)/2 pairs and selecting a union having a maximal value for the functional relation, or objective function, that reflects the criterion chosen by the investigator. By repeating this process until only one group remains, the complete hierarchical structure and a quantitative estimate of the loss associated with each stage in the grouping can be obtained. A general flowchart helpful in computer programming and a numerical example are included.

Keywords

FlowchartMathematicsFunction (biology)Relation (database)Mathematical optimizationReduction (mathematics)Group (periodic table)Process (computing)Scale (ratio)AlgorithmComputer scienceStatisticsCombinatoricsData mining

Affiliated Institutions

Related Publications

The Problem of Optimum Stratification

Abstract Abstract Although most applications of stratified sampling represent sampling from a finite population, π(N), consisting of k mutually exclusive sub-populations or stra...

1950 Scandinavian Actuarial Journal 229 citations

Publication Info

Year
1963
Type
article
Volume
58
Issue
301
Pages
236-244
Citations
18547
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

18547
OpenAlex
630
Influential
13406
CrossRef

Cite This

Joe H. Ward (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association , 58 (301) , 236-244. https://doi.org/10.1080/01621459.1963.10500845

Identifiers

DOI
10.1080/01621459.1963.10500845

Data Quality

Data completeness: 81%