Hubert and Arabie (H(C) and A)

Hubert and Arabie (H and A) developed a method to compare two different ways of dividing items into groups, such as comparing machine learning models' results. They introduced measures called the Adjusted Rand Index (ARI) that quantify how similar these groupings are, correcting for the possibility of agreement by chance. An ARI close to 1 indicates strong agreement between the partitions, while values near 0 suggest no more agreement than random chance. Their approach provides a standardized way to evaluate and compare clustering results, ensuring meaningful and reliable assessments of similarity.