Real applications of clustering

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K-means clustering uses different distance metrics (e.g., Euclidean, Manhattan, Angle). The most common approach is to use Euclidean distance, which is most useful for globularly distributed features (e.g., approximately Gaussian distributed variables). In general, how the distance is chosen for real applications of clustering? Can you use real-application cases to illustrate?

Reference no: EM133248367

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