Entropy K-Means Clustering With Feature Reduction Under Unknown Number of Clusters

The k-means algorithm with its extensions is the most used clustering method in the literature. But, the k-means and its various extensions are generally affected by initializations with a given number of clusters. On the other hand, most of k-means always treat data points with equal importance for...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.67736-67751
Main Authors: Sinaga, Kristina P., Hussain, Ishtiaq, Yang, Miin-Shen
Format: Article
Language:English
Subjects:
Algorithms
Cluster analysis
Clustering
Clustering algorithms
Data points
Entropy
entropy-k-means
feature reduction
feature weights
Indexes
k-means
Linear programming
number of clusters
Optimization
Partitioning algorithms
Reduction
Vector quantization
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Summary:The k-means algorithm with its extensions is the most used clustering method in the literature. But, the k-means and its various extensions are generally affected by initializations with a given number of clusters. On the other hand, most of k-means always treat data points with equal importance for feature components. There are several feature-weighted k-means proposed in literature, but, these feature-weighted k-means do not give a feature reduction behavior. In this paper, based on several entropy-regularized terms we can construct a novel k-means clustering algorithm, called Entropy-k-means, such that it can be free of initializations without a given number of clusters, and also has a feature reduction behavior. That is, the proposed Entropy-k-means algorithm can eliminate irrelevant features with feature reduction under free of initializations with automatically finding an optimal number of clusters. Comparisons between the proposed Entropy-k-means and other methods are made. Experimental results and comparisons actually demonstrate these good aspects of the proposed Entropy-k-means with its effectiveness and usefulness in practice.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3077622