Machine LearningLesson 15Free preview

ML Clustering

This lesson is part of A.I. · All levels

Summary

Clustering is an unsupervised learning technique for grouping similar data without labeling. Popular algorithms include K-Means which divide data into K groups based on proximity, and k-Nearest Neighbors which determine classes based on nearest neighbors. An example is the grouping of customers based on shopping behavior.

Key points

Clustering includes unsupervised learning because it doesn't use labels.
K-Means divides the data into K clusters based on the distance to the center of the cluster.
KNN specifies a new data class from the majority of its nearest neighbors.
Clustering is useful for segmenting customers and detecting anomalous groups.

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