Unsupervised K-Means Clustering Algorithm

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Abstract:

The k-means algorithm is generally the most known and used clustering method. 

There are various extensions of k-means to be proposed in the literature. 

Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. 

That is, the k-means algorithm is not exactly an unsupervised clustering method. 

In this paper, we construct an unsupervised learning schema for the k-means algorithm so that it is free of initializations without parameter selection and can also simultaneously find an optimal number of clusters.

That is, we propose a novel unsupervised k-means (U-k-means) clustering algorithm with automatically finding an optimal number of clusters without giving any initialization and parameter selection. 

The computational complexity of the proposed U-k-means clustering algorithm is also analyzed. 

Comparisons between the proposed U-k-means and other existing methods are made. 

Experimental results and comparisons actually demonstrate these good aspects of the proposed U-k-means clustering algorithm.

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https://ieeexplore.ieee.org/document/9072123

https://www.researchgate.net/publication/340813602_Unsupervised_K-Means_Clustering_Algorithm

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*k-means*