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Related Experiment Video

Updated: Apr 5, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Hierarchical Clustering With Prototypes via Minimax Linkage.

Jacob Bien¹, Robert Tibshirani²

¹Department of Statistics, Stanford University, Stanford, CA 94305.

Journal of the American Statistical Association

|August 11, 2015

Summary

Minimax linkage enhances hierarchical clustering interpretability by associating data prototypes with dendrogram nodes. This novel method offers theoretical benefits and practical applications in data analysis and visualization.

Keywords:

Agglomerative Dendrogram Unsupervised learning

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Published on: February 15, 2017

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Published on: March 1, 2022

Area of Science:

Data Science
Machine Learning
Computational Statistics

Background:

Agglomerative hierarchical clustering is widely used for dataset structure analysis.
Cluster distance measurement, or linkage, significantly impacts clustering outcomes.
Existing linkage methods may lack interpretability or possess undesirable properties.

Purpose of the Study:

To investigate minimax linkage, a novel and understudied hierarchical clustering linkage method.
To demonstrate the unique interpretability benefits of minimax linkage through associated data prototypes.
To evaluate the theoretical properties and practical utility of minimax linkage.

Main Methods:

Introduced and defined minimax linkage for agglomerative hierarchical clustering.
Proved theoretical properties, including resistance to inversions and robustness to perturbations.
Developed an efficient implementation of the minimax linkage algorithm.

Main Results:

Minimax linkage naturally associates data prototypes with dendrogram interior nodes, enhancing interpretability.
Demonstrated that minimax linkage dendrograms do not exhibit inversions, unlike centroid linkage.
Showcased minimax linkage's effectiveness on real-world datasets, including text and image data.

Conclusions:

Minimax linkage offers significant advantages in hierarchical clustering, particularly in interpretability and theoretical robustness.
The method provides a valuable tool for data analysis and visualization, enhancing understanding of complex datasets.
Further exploration of minimax linkage is warranted due to its unique properties and demonstrated utility.