Clustering

Both clustering and dimensionality reduction summarize the data. As we have just discussed, dimensionality reduction compresses the data by representing it using new, fewer features that capture the most relevant information. Clustering algorithms, in contrast, assign existing observations to subgroups that consist of similar data points.

Clustering can serve to better understand the data through the lens of categories learned from continuous variables. It also permits you to automatically categorize new objects according to the learned criteria. Examples of related applications include hierarchical taxonomies, medical diagnostics, and customer segmentation. Alternatively, clusters can be used to represent groups as prototypes, using, for example, the midpoint of a cluster as the best representatives of learned grouping. An example application includes image compression.

Clustering algorithms differ with respect to their strategy of identifying groupings:

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