Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads

By Mark Ming-Tso Chiang, Boris G. Mirkin. Journal of Classification, 2010

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How much can k-means be improved by using better initialization and repeats? (2019, background): “Chiang and Mirkin [55] recalculate all the centroids after updating the partitions, and the next centroid is selected as the farthest from the recently added centroid.”
Recovering the number of clusters in data sets with noise features using feature rescaling factors (2015, methodology): “Difficulties in the estimation of the number of clusters raised by the presence of noise features in a data set have been considered before [12], however, there is still a view that the issue raised by noise features deserves more consideration [7].”
Exploratory Learning (2013, background): “There has also been substantial work in the past to automatically decide the right “number of clusters” in unsupervised learning [11,22,15,7,19,27].”
Constrained Semi-supervised Learning in the Presence of Unanticipated Classes (2016, background): “There has also been substantial work in the past to automatically decide the right “number of clusters” in unsupervised learning [30, 40, 59, 88, 102, 138].”
Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering (2012, methodology): “A number of these approaches, including those in [27–29], have been tested on synthetic datasets comprising a number of Gaussian clusters, possibly well elongated and intermixed , by Chiang and Mirkin [10].”