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Hierarchical Clustering Schemes
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.”
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Citations
Cited by
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392
Tversky, A. (1977) · Psychological Review · doi:10.1037/0033-295x.84.4.327 · Cited by 7,414
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Representational similarity analysis-connecting the branches of systems neuroscience
Kriegeskorte, N. (2008) · Frontiers in Systems Neuroscience · doi:10.3389/neuro.06.004.2008 · Cited by 3,355
Borgatti, S. P. (2005) · Social Networks · doi:10.1016/j.socnet.2004.11.008 · Cited by 3,191
Metadata
- Authors
- Johnson, S. C.
- Venue
- Psychometrika
- Publisher
- Cambridge University Press (CUP)
- Published
- 1967-09
- Volume
- 32
- Issue
- 3
- Pages
- 241-254
- ISSN
- 0033-3123 (ISSN-L)
- 1860-0980
- OpenAlex
- W1998871699
- Cited by
- 4,897
- References
- 0
- FWCI
- 21.60
- Type
- journal article
- Language
- en
- Open access
- closed
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