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Hierarchical clustering - Wikipedia

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In data mining and statistics, hierarchical clustering[1] (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories: In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering[1] are usually presented in a dendrogram. Hierarchical clustering has the distinct advantage that any valid measure of distance can be used. In fact, the observations themselves are not required: all that is used is a matrix of distances. On the other hand, except for the special case of single-linkage distance, none of the algorithms (except exhaustive search in 𝑂 ( 2 𝑛 ) ) can be guaranteed to find the optimum solution.[citation needed] The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of 𝑂 ( 𝑛 3 ) and requires Ω ( 𝑛 2 ) memory, which makes it too slow for even medium d

Hierarchical clustering - Wikipedia Jump to content From Wikipedia, the free encyclopedia Statistical method in data analysis "SLINK" redirects here. For the online magazine, see Slink . Part of a series on Machine learning and data mining Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised learning Reinforcement learning Meta-learning Online learning Batch learning Curriculum learning Rule-based learning Neuro-symbolic AI Neuromorphic engineering Quantum machine learning Problems Classification Generative modeling Regression Clustering Dimensionality r

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