Cluster analysis groups observations using similarities or dissimilarities without supplied class labels. A distance choice specifies which features and scales matter. Agglomerative hierarchical clustering merges groups recursively, producing partitions at different resolutions rather than one intrinsically determined number of groups.
Cluster analysis is a statistical technique used to group a set of objects or data points into clusters based on their similarities or distances from one another. The main goal of cluster analysis is to identify patterns within a dataset and to categorize data points into groups so that points within the same group (or cluster) are more similar to each other than they are to points in other groups.
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