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What is Data Clustering Techniques Overview about?

The document discusses different clustering algorithms including K-means clustering and K-medoids clustering. - K-means clustering aims to partition data points into K number of clusters where each data point belongs to the cluster with the nearest mean. The algorithm works by assigning data points to centroids and updating the centroid locations iteratively. - K-medoids clustering is similar but chooses actual data points as cluster centroids rather than mean points, allowing arbitrary distance measure

Author

kedar damkondwar

Language

EN