A close‐up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation
The misclassification error distance and the adjusted Rand index are two of the most common criteria used to evaluate the performance of clustering algorithms. This paper provides an in‐depth comparison of the two criteria, with the aim of better understand exactly what they measure, their propertie...
Gespeichert in:
Veröffentlicht in: | British journal of mathematical & statistical psychology 2021-05, Vol.74 (2), p.203-231 |
---|---|
1. Verfasser: | |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | The misclassification error distance and the adjusted Rand index are two of the most common criteria used to evaluate the performance of clustering algorithms. This paper provides an in‐depth comparison of the two criteria, with the aim of better understand exactly what they measure, their properties and their differences. Starting from their population origins, the investigation includes many data analysis examples and the study of particular cases in great detail. An exhaustive simulation study provides insight into the criteria distributions and reveals some previous misconceptions. |
---|---|
ISSN: | 0007-1102 2044-8317 |
DOI: | 10.1111/bmsp.12212 |