Hierarchical clustering: Difference between revisions

Revision as of 07:00, 30 August 2024

Revision as of 16:11, 30 August 2024

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== Cluster Linkage ==

In order to decide which clusters should be combined (for agglomerative), or where a cluster should be split (for divisive), a measure of dissimilarity between sets of observations is required. In most methods of hierarchical clustering, this is achieved by use of an appropriate [[distance]] ''d'', such as the Euclidean distance, between ''single'' observations of the data set, and a linkage criterion, which specifies the dissimilarity of ''sets'' as a function of the pairwise distances of observations in the sets <ref>{{Cite journal |last=Wani |first=Aasim Ayaz |date=2024-08-29 |title=Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions |url=https://peerj.com/articles/cs-2286/ |journal=PeerJ Computer Science |language=en |volume=10 |pages=e2286 |doi=10.7717/peerj-cs.2286 |doi-access=free |issn=2376-5992}}</ref>. The choice of metric as well as linkage can have a major impact on the result of the clustering, where the lower level metric determines which objects are most [[similarity measure|similar]], whereas the linkage criterion influences the shape of the clusters. For example, complete-linkage tends to produce more spherical clusters than single-linkage.

The linkage criterion determines the distance between sets of observations as a function of the pairwise distances between observations.