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DOI: 10.14569/IJACSA.2021.0121096
PDF

Joint Deep Clustering: Classification and Review

Author 1: Arwa Alturki
Author 2: Ouiem Bchir
Author 3: Mohamed Maher Ben Ismail

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 10, 2021.

  • Abstract and Keywords
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Abstract: Clustering is a fundamental problem in machine learning. To address this, a large number of algorithms have been developed. Some of these algorithms, such as K-means, handle the original data directly, while others, such as spectral clustering, apply linear transformation to the data. Still others, such as kernel-based algorithms, use nonlinear transformation. Since the performance of the clustering depends strongly on the quality of the data representation, representation learning approaches have been extensively researched. With the recent advances in deep learning, deep neural networks are being increasingly utilized to learn clustering-friendly representation. We provide here a review of existing algorithms that are being used to jointly optimize deep neural networks and clustering methods.

Keywords: Clustering; deep learning; deep neural network; representation learning; clustering loss; reconstruction loss

Arwa Alturki, Ouiem Bchir and Mohamed Maher Ben Ismail, “Joint Deep Clustering: Classification and Review” International Journal of Advanced Computer Science and Applications(IJACSA), 12(10), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121096

@article{Alturki2021,
title = {Joint Deep Clustering: Classification and Review},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121096},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121096},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {10},
author = {Arwa Alturki and Ouiem Bchir and Mohamed Maher Ben Ismail}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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