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What is Clustering Techniques and Algorithms about?

Clustering is an unsupervised task that groups similar instances into clusters, with applications in customer segmentation, data analysis, and anomaly detection. The document discusses popular clustering algorithms like K-Means and DBSCAN, detailing their processes, initialization methods, and performance metrics such as inertia and silhouette score. It also highlights limitations of K-Means, including challenges in determining the optimal number of clusters and its poor performance with non-spherical clust

Author

leonhardkwahle

Language

EN