Unsupervised K-Means Clustering Algorithm

• Published in: IEEE access Vol. 8; pp. 80716 - 80727
• Main Authors: Sinaga, Kristina P., Yang, Miin-Shen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cluster analysis; Clustering; Clustering algorithms; Clustering methods; Entropy; Indexes; initializations; K-means; Linear programming; Machine learning; Machine learning algorithms; number of clusters; Parameters; Pattern recognition; Unsupervised k-means (U-k-means); Unsupervised learning; unsupervised learning schema; Vector quantization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2988796

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.