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A close‐up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation
Author(s) -
Chacón José E.
Publication year - 2021
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/bmsp.12212
Subject(s) - rand index , cluster analysis , index (typography) , statistics , computer science , measure (data warehouse) , data mining , population , mathematics , medicine , world wide web , environmental health
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.