Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering

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A review of moving object trajectory clustering algorithms (2016, methodology): “Two improved editions are given in Amorim and Mirkin (2012) and k-medoids (Park and Jun 2009). K-means algorithm has been used in many clustering tasks so far. Its core idea is to randomly find k clustering centers, and then iteratively group the data point to the nearest clustering center according”
Recovering the number of clusters in data sets with noise features using feature rescaling factors (2015, methodology): “Due to its original motivation by the statistical theory behind F-test comparing groups in multivariate ANOVA, this index uses the Euclidean distance exclusively.”
Neural network-based clustering using pairwise constraints (2015, methodology): “Thus, the scheme is not the same as the semi-supervised clustering framework in previous works (Wagstaff et al., 2001; Bilenko et al., 2004; De Amorim & Mirkin, 2012) where their unlabeled data contribute to calculating the centers of the clusters.”