Review on determining number of Cluster in K-Means Clustering

By Trupti M. Kodinariya, Prashant R. Makwana. 2013

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A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm (2020, methodology): “The cluster number obtained by using the elbow method is a subjective result since it is a visual method[14], and it does not give a measurement metric to show which elbow point is the optimal explicitly.”
Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks (2020, methodology): “We perform a k-means cluster where the number of clusters is decided according to the elbow method (Kodinariya and Makwana, 2013) and following the results of the PCA.”
Research paper classification systems based on TF-IDF and LDA schemes (2019, methodology): “One method to validate the number of clusters is to use the Elbow scheme [35].”
Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling (2019, methodology): “ster assignments no longer change. To determine the optimal number of clusters k for the given observations, the elbow method is used which calculates the distortions for dierent numbers of clusters [63]. As shown in Figure12, the optimal value for kis determined as k= 4 where adding another cluster”