/Entropy-k-means

Code of Entropy-k-means: Entropy K-Means Clustering With Feature Reduction Under Unknown Number of Clusters

Primary LanguageMATLABMIT LicenseMIT

Entropy-k-means

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.

Published in IEEE Access'21

Simulations

Results

In case the repository or the publication was helpful in your work, please use the following to cite the original paper,

 @article{sinaga2021entropy,

  title={Entropy K-means clustering with feature reduction under unknown number of clusters},

  author={Sinaga, Kristina P and Hussain, Ishtiaq and Yang, Miin-Shen},

  journal={IEEE Access},

  volume={9},

  pages={67736--67751},

  year={2021},

  publisher={IEEE}

}
}

Author of Code

Kristina P. Sinaga ✉️: kristinasinaga57@yahoo.co.id