Unsupervised learning focusing on modern clustering methods. Topics may include: distance metrics, linkage methods, hierarchical clustering, k-medoid clustering, block clustering, two-way clustering, heat maps, self-organizing maps, kernel-based clustering, ensemble-based clustering, and fuzzy clustering.

Prerequisites: DATA 512 or permission of department chair.

4 Credits

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