K-means and Hierarchical Clustering

K-means and Hierarchical Clustering

Tutorial Slides by Andrew Moore

K-means is the most famous clustering algorithm. In this tutorial we review just what it is that clustering is trying to achieve, and we show the detailed reason that the k-means approach is cleverly optimizing something very meaningful. Oh yes, and we'll tell you (and show you) what the k-means algorithm actually does. You'll also learn about another famous class of clusterers: hierarchical methods (much beloved in the life sciences). Phrases like "Hierarchical Agglomerative Clustering" and "Single Linkage Clustering" will be bandied about.

Download Tutorial Slides (PDF format)

Powerpoint Format: The Powerpoint originals of these slides are freely available to anyone who wishes to use them for their own work, or who wishes to teach using them in an academic institution. Please email Andrew Moore at awm@cs.cmu.edu if you would like him to send them to you. The only restriction is that they are not freely available for use as teaching materials in classes or tutorials outside degree-granting academic institutions.

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