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

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
Main Authors: Kristina P. Sinaga, Ishtiaq Hussain, Miin-Shen Yang
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Clustering
k-means
entropy
feature weights
feature reduction
number of clusters
Online Access:https://ieeexplore.ieee.org/document/9423951/

Internet

https://ieeexplore.ieee.org/document/9423951/

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