Unsupervised K-Means Clustering Algorithm
By Kristina P. Sinaga, Miin-Shen Yang. IEEE Access, 2020
1,791 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems
View Paper: https://www.semanticscholar.org/paper/b5887d18420e8ac4f4fa4c83c4952138fd956702
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Citations in the corpus
- The effects of data quality on machine learning performance on tabular data (2022, neutral): “…used categories of clustering algorithms [59]: the Gaussian mixture clustering algorithm [58] from the distribution-based family; the 𝑘 -means [66] and the 𝑘 -prototypes [35] algorithms from the centroid-based family; the agglomerative clustering algorithm [17] from the hierarchical…”
- The Effects of Data Quality on Machine Learning Performance (2022, methodology): “…used categories of clustering algorithms [58]: The Gaussian Mixture Clustering al-gorithm [57] from the distribution-based family, the k-Means [65] and the k-Proto-types [36] algorithms from the centroid-based family, the Agglomerative Clustering algorithm [17] from the hierarchical family,…”