Selection of K in K-means clustering

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Automatic Database Management System Tuning Through Large-scale Machine Learning (2017, methodology): “We use a simple heuristic [40] to fully automate this selection process and approximate K.”
Capturing value from big data – a taxonomy of data-driven business models used by start-up firms (2016, result): “Several different approaches exist to determine the number of clusters, and it is advisable to compare the results of different methods (Pham et al., 2005; Mooi and Sarstedt, 2011; Han et al., 2011).”
Improvement of SCS-CN Initial Abstraction Coefficient in the Czech Republic: A Study of Five Catchments (2020, methodology): “Cluster Analysis Using the methodology introduced by Pham et al. (2005), the optimal number of clusters was determined to be two for the experimental watersheds.”
Neural context embeddings for automatic discovery of word senses (2015, methodology): “We cluster the selected instances using k - means clustering with the heuristic of Pham et al. (2005) for choosing k .”