A Rapid Review of Clustering Algorithms
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929210143014912 |
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| author | Yin, Hui Aryani, Amir Petrie, Stephen Nambissan, Aishwarya Astudillo, Aland Cao, Shengyuan |
| author_facet | Yin, Hui Aryani, Amir Petrie, Stephen Nambissan, Aishwarya Astudillo, Aland Cao, Shengyuan |
| contents | Clustering algorithms aim to organize data into groups or clusters based on the inherent patterns and similarities within the data. They play an important role in today's life, such as in marketing and e-commerce, healthcare, data organization and analysis, and social media. Numerous clustering algorithms exist, with ongoing developments introducing new ones. Each algorithm possesses its own set of strengths and weaknesses, and as of now, there is no universally applicable algorithm for all tasks. In this work, we analyzed existing clustering algorithms and classify mainstream algorithms across five different dimensions: underlying principles and characteristics, data point assignment to clusters, dataset capacity, predefined cluster numbers and application area. This classification facilitates researchers in understanding clustering algorithms from various perspectives and helps them identify algorithms suitable for solving specific tasks. Finally, we discussed the current trends and potential future directions in clustering algorithms. We also identified and discussed open challenges and unresolved issues in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07389 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Rapid Review of Clustering Algorithms Yin, Hui Aryani, Amir Petrie, Stephen Nambissan, Aishwarya Astudillo, Aland Cao, Shengyuan Machine Learning Artificial Intelligence 68-02 I.2.0 Clustering algorithms aim to organize data into groups or clusters based on the inherent patterns and similarities within the data. They play an important role in today's life, such as in marketing and e-commerce, healthcare, data organization and analysis, and social media. Numerous clustering algorithms exist, with ongoing developments introducing new ones. Each algorithm possesses its own set of strengths and weaknesses, and as of now, there is no universally applicable algorithm for all tasks. In this work, we analyzed existing clustering algorithms and classify mainstream algorithms across five different dimensions: underlying principles and characteristics, data point assignment to clusters, dataset capacity, predefined cluster numbers and application area. This classification facilitates researchers in understanding clustering algorithms from various perspectives and helps them identify algorithms suitable for solving specific tasks. Finally, we discussed the current trends and potential future directions in clustering algorithms. We also identified and discussed open challenges and unresolved issues in the field. |
| title | A Rapid Review of Clustering Algorithms |
| topic | Machine Learning Artificial Intelligence 68-02 I.2.0 |
| url | https://arxiv.org/abs/2401.07389 |