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12. Algorithm Summary > Clustering

Clustering

Hierarchical clustering and K-means clustering are unsupervised learning techniques, meaning they don’t require examples for training data because they don’t attempt to make predictions. Chapter 3 looked at how to take a list of top bloggers and automatically cluster them so you could see which ones naturally fell into groups that write about similar subjects or use similar words.

Hierarchical Clustering

Clustering works on any set of items that have one or more numerical properties. The example in Chapter 3 used word counts for the different blogs, but any set of numbers can be used for clustering. To demonstrate how the hierarchical clustering algorithm works, consider a simple table of items (some letters of the alphabet) and some numerical properties (Table 12-7).


  

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