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IEEE/ICACT20220359 Slide.08        [Big Slide]       [YouTube]	Oral Presentation
	The K-medoids algorithm is a type of clustering algorithm that emerged to solve the noise and outliers of the K-means algorithm. The K-medoids algorithm is more efficient in the time to select heads than the K-means algorithm, in the spatial complexity of cluster superposition, and The center value of the coordinates is calculated like K-means, but unlike K-means, which is calculated as the center of all coordinates, the actual coordinates most similar to the center value of the coordinates are used as the center.

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