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A scalable k-medoids clustering via whale optimization algorithm
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DOI：10.1016/j.array.2025.100599.png)
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Abstract
En 中文
• We introduced a novel WOA-kMedoids unsupervised clustering algorithm. • WOA-kMedoids enhances the scalability of k-Medoids for big-data. • Linear computational time in data size is achieved by introducing the WOA. • WOA-kMedoids was more efficient than Partitioning Around Medoids for big data.
Keywords:
Unsupervised clustering
Computationally efficient algorithm
Whale optimization algorithm
Partitioning around medoids
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