Unsupervised K-Means Clustering Algorithm

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 alwa...

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Veröffentlicht in:IEEE access Jg. 8; S. 80716 - 80727
Hauptverfasser: Sinaga, Kristina P., Yang, Miin-Shen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2988796