Addressing limitations of the K-means clustering algorithm: outliers, non-spherical data, and optimal cluster selection
Clustering is essential in data analysis, with K-means clustering being widely used for its simplicity and efficiency. However, several challenges can affect its performance, including the handling of outliers, the transformation of non-spherical data into a spherical form, and the selection of the...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2024-08-01
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| Series: | AIMS Mathematics |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.20241222?viewType=HTML |