Clustering: K-Means
Clustering is the most well-known unsupervised learning technique. It finds structure in unlabeled data by identifying similar groups.
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Key Concepts
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K-Means: Inertia
Unsupervised Learning Basics
K-Means Algorithm: Intro
K-Means Algorithm: 2nd Step
Scikit-Learn Datasets
K-Means Using Scikit-Learn
Cross Tabulation Overview
K-Means: Reaching Convergence
K-Means: Inertia
K-Means: Inertia
Inertia measures how well a dataset was clustered by K-Means. It is calculated by measuring the distance between each data point and its centroid, squaring this distance, and summing these squares across one cluster.
A good model is one with low inertia AND a low number of clusters (K). However, this is a tradeoff because as K increases, inertia decreases.
To find the optimal K for a dataset, use the Elbow method; find the point where the decrease in inertia begins to slow. K=3 is the “elbow” of this graph.
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Clustering: K-Means
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