Introduction to Clustering

Course video 32 of 39

This module introduces clustering, where data points are assigned to larger groups of points based on some specific property, such as spatial distance or the local density of points. While humans often find clusters visually with ease in given data sets, computationally the problem is more challenging. This module starts by exploring the basic ideas behind this unsupervised learning technique, as well as different areas in which clustering can be used by businesses. Next, one of the most popular clustering techniques, K-means, is introduced. Next the density-based DB-SCAN technique is introduced. This module concludes by introducing the mixture models technique for probabilistically assigning points to clusters.

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