A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering
1Departamento de Matemáticas, Universidad de Extremadura, Badajoz, Spain.
Abstract:
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.
Related Concept Videos
Detection of Gross Error: The Q Test
Receiver Operating Characteristic Plot
Statistical Analysis: Overview
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Spearman's Rank Correlation Test
Spearman's test calculates correlation by...
Quantifying and Rejecting Outliers: The Grubbs Test
McNemar's Test


