An efficient k'-means clustering algorithm

By Krista Rizman Zalik. Pattern Recognition Letters, 2008

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Machine Learning Algorithms: A Review (2022, neutral): “Unsupervised learning generally focuses on clustering and dimensional reduction problems [3, 16].”
Scenario Reduction Applied to Geostatistical Simulations (2013, background): “According to Kanungo et al. (2002), one of the difficulties is that the distances have to be recalculated with each iteration.”
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis (2010, methodology): “Zalik has introduced a k-means algorithm that performs correct clustering without pre-assigning the exact number of clusters [3].”
Stochastic Simulation of Patterns Using Distance-Based Pattern Modeling (2010, methodology): “There has been many attempts on using kd-trees in k -means clustering (Alsabti, 1998; Kanungo et al., 2000).”
The Effectiveness of Lloyd-Type Methods for the k-Means Problem (2006, background): “1980] (see also Gersho and Gray [1992], Gray and Neuhoff [1998], and Jain et al. [1999], and the references therein); they remain so today, and are still being improved [Alsabti et al. 1998; Pelleg and Moore 1999; Phillips 2002; Kanungo et al. 2002].”
Clustering with Bregman Divergences: an Asymptotic Analysis (2016, background): “There is an extensive literature on various aspects and properties of k-means clustering, including applications and theoretical analysis [2, 13, 23].”