An extensive comparative study of cluster validity indices

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Statistical power for cluster analysis (2020, background): “Even studies aimed at identifying factors that impact cluster algorithm performance have used, perhaps unrealistically, large cluster separation [19, 20].”
Recovering the number of clusters in data sets with noise features using feature rescaling factors (2015, methodology): “active field of research [7, 1, 22, 29], but due to the lack of a generally accepted definition of what a “cluster” is there are no unified standards against which it can be assessed.”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, positive): “[372] have demonstrated the use of 30 cluster validity indices in 720 synthetic and 20 real datasets.”
dbscan: Fast Density-Based Clustering with R (2019, background): “…variance’, where the underlying assumption is made that ideal clusters are found by minimizing some measure of intra-cluster variance (often referred to as cluster cohesion) and maximizing the inter-cluster variance (cluster separation) (Arbelaitz, Gurrutxaga, Muguerza, P´erez, and Perona 2013).”