A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation
This work clarifies evaluation metrics for clustering, which is useful for researchers in data analysis and machine learning, but it is incremental as it focuses on comparing existing methods.
The paper compares the misclassification error distance and adjusted Rand index for evaluating clustering algorithms, analyzing their properties and differences through data examples and simulations, revealing previous misconceptions.
The misclassification error distance and the adjusted Rand index are two of the most commonly used criteria to evaluate the performance of clustering algorithms. This paper provides an in-depth comparison of the two criteria, aimed to 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 allows inspecting the criteria distributions and reveals some previous misconceptions.