Grouping related examples, particularly during unsupervised learning. Once all the examples are grouped, a human can optionally supply meaning to each cluster. Many clustering algorithms exist. For example, the k-means algorithm clusters examples based on their proximity to a centroid, as in the following diagram: A human researcher could then review the clusters and, for example, label cluster 1 as “dwarf trees” and cluster 2 as “full-size trees.” As another example, consider a clustering algorithm based on an example’s distance from a center point, illustrated as follows: The post What is clustering in Machine Learning? appeared first on Data Science PR. Originally from Machine Learning & AI – Data Science PR https://ift.tt/2WMYFjR
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