Cited by 702Data Mining and Machine Learning ApplicationsStatistical Methods and ApplicationsMachine Learning and Data Classification

A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science

Alloghani, Mohamed, Al-Jumeily, Dhiya, Mustafina, Jamila, Hussain, Abir, Aljaaf, Ahmed J.·2020·Unsupervised and Semi-Supervised Learning

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Crossref · 10.1007/978-3-030-22475-2_1

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116 of 116 references verified0 citation errors · 0 not checkable

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  • The most common clustering models are k-means and the Hidden Markov Model, which are commonly used to secure vehicular networks.

    “The most common clustering models are k-means and the Hidden Markov Model, which are commonly used to secure vehicular networks [52,54].”
  • Supervised learning necessitates manual labelling of data, which slows system performance.

    “Supervised learning necessitates manual labelling of data, which slows the system's performance [8].”

    From Self-supervised learning: A succinct review (2023) · cites this paper as [8]

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Authors
Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., & Aljaaf, A. J.
Affiliations
  • Abu Dhabi Health Services (AE)
  • Kazan Federal University (RU)
  • Liverpool John Moores University (GB)
  • University of Anbar (IQ)
Corresponding author
Alloghani, Mohamed
Venue
Unsupervised and Semi-Supervised Learning
Published
2019-09-05
Pages
3-21
ISSN
  • 2522-848X (ISSN-L)
  • 2522-8498
OpenAlex
W2971565213
Cited by
702
References
116
Citations by year
2026: 80 · 2025: 142 · 2024: 176 · 2023: 162 · 2022: 79 · 2021: 50 · 2020: 10 · 2019: 1 · 2012: 1
FWCI
6.12
Type
journal article
Language
en
Open access
closed
Locations
Unsupervised and semi-supervised learning (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Keywords
supervised learning, unsupervised learning, data science, decision tree, support vector machine, naive Bayes, K-means clustering, hierarchical clustering, principal component analysis, ensemble learning, reinforcement learning

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