A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms

In the field of data mining, clustering has shown to be an important technique. Numerous clustering methods have been devised and put into practice, and most of them locate high-quality or optimum clustering outcomes in the field of computer science, data science, statistics, pattern recognition, ar...

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Vydané v:Applied sciences Ročník 13; číslo 6; s. 3529
Hlavní autori: Pitafi, Shahneela, Anwar, Toni, Sharif, Zubair
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.03.2023
Predmet:
Algorithms
Artificial intelligence
Big Data
challenges in clustering algorithms
Cluster analysis
Clustering
clustering algorithms
Computer science
Data mining
Datasets
Machine learning
Methods
Middleware
Taxonomy
taxonomy of clustering algorithms
Germany
ISSN:2076-3417, 2076-3417
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Shrnutí:In the field of data mining, clustering has shown to be an important technique. Numerous clustering methods have been devised and put into practice, and most of them locate high-quality or optimum clustering outcomes in the field of computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. This research provides a modern, thorough review of both classic and cutting-edge clustering methods. The taxonomy of clustering is presented in this review from an applied angle and the compression of some hierarchical and partitional clustering algorithms with various parameters. We also discuss the open challenges in clustering such as computational complexity, refinement of clusters, speed of convergence, data dimensionality, effectiveness and scalability, data object representation, evaluation measures, data streams, and knowledge extraction; scientists and professionals alike will be able to use it as a benchmark as they strive to advance the state-of-the-art in clustering techniques.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app13063529