The Rand Index can be used for comparing the results from multiple clustering algorithms. It is simply a ratio of the number of agreeing pairs of observations, which include both those assigned to the same cluster in both iterations as well as those assigned to different clusters in both iterations, over the total number of pairs of data (nC2). Values close to 0 indicate strong discordance between algorithms, meaning the observations are assigned to clusters by random chance, and a value of 1 indicates perfect agreement of cluster assignments, which is preferred.