K-Means Clustering Limitations

Medium

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Multiple Choice

K-means with more clusters (higher K) to approximate the shapes DBSCAN, which discovers clusters of arbitrary shapes based on density K-means with Manhattan distance instead of Euclidean distance Running K-means multiple times with different initializations

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