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Similarity, Neighbors, and Clusters: 2nd half (Clustering (Clustering is…
Similarity, Neighbors, and Clusters: 2nd half
Clustering
Clustering is another application of our fundamental notion of similarity. The basic idea is that we want to find groups of objects (consumers, businesses, whiskeys, etc.), where the objects within groups are similar, but the objects in different groups are not so similar.
Hierarchical Clustering: It is a clustering because it groups the points by their similarity. Notice that the only overlap between clusters is when one cluster contains other clusters.
hierarchical clustering doesn’t just create “a cluster‐ ing,” or a single set of groups of objects. It creates a collection of ways to group the points. To see this clearly, consider “clipping” the dendrogram with a horizontal line, ignoring everything above the line.
An advantage of hierarchical clustering is that it allows the data analyst to see the groupings—the “landscape” of data similarity—before deciding on the number of clus‐ ters to extract.
Tree of Life
arge hierarchical trees are often displayed radially to conserve space, as is done here
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