Clustering validity checking methods: part II

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Relative clustering validity criteria: A comparative overview (2010, background): “Some of the most wellknown validity measures, also referred to as relative validity (or quality) criteria, are possibly the Davies–Bouldin index [7,15], the variance ratio criterion—VRC (so-called Calinski–Harabasz index) [2,16], Dunn’s index [17,18], and the silhouette width criterion (SWC) [1,2,19”
A survey of techniques for internet traffic classification using machine learning (2008, methodology): “The basic idea is to evaluate a clustering structure by comparing it to the ones using the same algorithm but with different parameter values [39].”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, methodology): “I Index [112], Rand index [95], Jaccard coefficient [95], Folkes and Mallows index [1], Hubert0s Γ statistic [95], SD Index [298], S-Dbw”
Application and Evaluation of a Hierarchical Patch Clustering Method for Remote Sensing Images (2016, background): “2) Internal Evaluation: Suggested by [26] and [27], not”
TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data (2014, methodology): “The expected cellular prevalence was computed using the tumor contribution from each individual sample making up the simulated mixture (Supplemental Methods; Supplemental Table 3B–”