Hierarchical clustering schemes

Clicks: 401
ID: 116269
1970
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #1 of 4 articles by views in psychometrika

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Techniques for partitioning objects into optimally homogeneous groups on the basis of empirical measures of similarity among those objects have received increasing attention in several different fields. This paper develops a useful correspondence between any hierarchical system of such clusters, and a particular type of distance measure. The correspondence gives rise to two methods of clustering that are computationally rapid and invariant under monotonic transformations of the data. In an explicitly defined sense, one method forms clusters that are optimally “connected,” while the other forms clusters that are optimally “compact.”
Reference Key
johnson1970psychometrikahierarchical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Stephen C. Johnson;Stephen C. Johnson;
Journal psychometrika
Year 1970
DOI
doi:10.1007/BF02289588
URL
https://dx.doi.org/10.1007/BF02289588
Keywords
assessment psychometrics Humanities law statistical theory and methods testing and evaluation statistics for social sciences

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.