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Updated: Jul 26, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

On parsimony and clustering.

Frédérique Oggier¹, Anwitaman Datta²

¹School of Physical & Mathematical Sciences, Nanyang Technological University Singapore, Singapore.

Peerj. Computer Science

|June 22, 2023

Summary

This summary is machine-generated.

Parsimonious cladograms, used for non-biological data analysis, differ from hierarchical clustering dendrograms. Some datasets show incompatibility between these clustering methods, impacting data analysis.

Keywords:

Clustering Parsimony

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Area of Science:

Computational Biology
Data Analysis
Clustering Algorithms

Background:

Parsimonious cladograms, originally for evolutionary biology, are increasingly applied to diverse non-biological data.
These cladograms are used in analyzing viruses and predicting protein structures.
Hierarchical clustering is a common method for data grouping.

Purpose of the Study:

To compare parsimony-optimized cladograms with dendrograms from single linkage hierarchical clustering.
To investigate the compatibility and differences between these two clustering approaches.

Main Methods:

Parsimonious cladogram optimization.
Single linkage hierarchical clustering.
Comparative analysis using F-scores.

Main Results:

Identified datasets where hierarchical clustering dendrograms are incompatible with parsimony optimization.
Demonstrated distinct clustering outcomes between the two methods.

Conclusions:

Parsimonious cladograms and single linkage hierarchical clustering are not interchangeable.
The choice of method can significantly affect data clustering results, especially for specific datasets.