Unsupervised Deep Learning: Taxonomy and algorithms

Abstract

Clustering is a fundamental challenge in many data-driven application fields and machine learning techniques. The data distribution determines the quality of the outcomes, which has a significant impact on clustering performance. As a result, deep neural networks can be used to learn more accurate data representations for clustering. Many recent studies have focused on employing deep neural networks to develop a clustering-friendly representation, which has resulted in a significant improvement in clustering performance. We present a systematic survey of clustering with deep learning in this study. Then, a taxonomy of deep clustering is proposed, as well as some sample algorithms for our overview. Finally, we discuss some exciting future possibilities for clustering using deep learning and offer some remarks.

Authors

  • Aida Chefrour Computer Science Department, Mohamed Cherif Messaadia University, Souk Ahras, Algeria and LISCO Laboratory, Computer Science Department, Badji Mokhtar University, B.P-12 Annaba, 23000, Algeria
  • Labiba Souici-Meslati LISCO Laboratory, Computer Science Department, Badji Mokhtar University, B.P-12 Annaba, 23000, Algeria

DOI:

https://doi.org/10.31449/inf.v46i2.3820

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Published

06/15/2022

How to Cite

Chefrour, A., & Souici-Meslati, L. (2022). Unsupervised Deep Learning: Taxonomy and algorithms. Informatica, 46(2). https://doi.org/10.31449/inf.v46i2.3820

Issue

Vol. 46 No. 2 (2022)

Section

Overview papers

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