Quality Scheme Assessment in the Clustering Process

By Maria Halkidi, Michalis Vazirgiannis, Yannis Batistakis. European Conference on Principles of Data Mining and Knowledge Discovery, 2000

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On Clustering Validation Techniques (2001, methodology): “Also, the influence of the maximum number of clusters cmax, related to the weighting factor, in the selection of the optimal clustering scheme is discussed in Halkidi et al. (2000).”
Understanding of Internal Clustering Validation Measures (2010, background): “The idea of SD index (SD) [15] is based on the concepts of the average scattering and the total separation of clusters.”
Cluster validity measurement techniques (2006, background): “The base measurements of SD validity index [10] are the average scattering and total separation of clusters.”
NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set (2014, methodology): “"sdindex" (Halkidi et al. 2000) Minimum value of the index 30.”
Density-Based Clustering Validation (2014, background): “The SD and S_Dbw measures [17, 14] have similar definitions, based on concepts of scattering inside clusters and separation between clusters considering the variance and distance between centroids of the clusters.”
Internal versus External cluster validation indexes (2011, background): “Currently, cluster validity indexes research has drawn attention as a means to give a solution [7].”
Clustering validity assessment: finding the optimal partitioning of a data set (2001, methodology): “We consider the known validity indices proposed in the literature, such as RS-RMSSTD[20], DB[22] and the recent one SD[23].”