Machine learning

Hierarchical Clustering

Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.

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Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI: 10.1080/01621459.1963.10500845 ↗

Referenced by

Affinity Propagation Bayesian Cluster Analysis Bayesian Hierarchical Clustering Community Detection DBSCAN DTW Barycenter Averaging Explainable K-Means Formal Concept Analysis Gaussian Mixture Model K-means K-Means Clustering Mean Shift Online K-means OPTICS Principal Component Analysis Robust Hierarchical Clustering Robust k-means Spectral Clustering Stochastic Block Model

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