Algorithms that group unlabeled data points by similarity, such as OPTICS, mean shift, complete-linkage clustering, UPGMA and WPGMA. An algorithm belongs here when its cited article calls it a clustering method or a cluster analysis algorithm. It excludes classifiers that learn from labeled examples. A member is placed here by what its own cited article states about it, not by the name of the algorithm or by the uses it is put to today, so each entry sits in the group its source describes it as belonging to.
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Clustering Algorithms
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OPTICS algorithm (Wikipedia)
Has Member: OPTICS Algorithm, Lead sectionQuote, Has Member: OPTICS Algorithm, Lead section
algorithm for finding density-based clusters in spatial data
Wikipedia: Complete-linkage clustering
Has Member: Complete-linkage clustering, Lead sectionQuote, Has Member: Complete-linkage clustering, Lead section
methods of agglomerative hierarchical clustering
Wikipedia: UPGMA
Has Member: UPGMA, Lead sectionQuote, Has Member: UPGMA, Lead section
agglomerative (bottom-up) hierarchical clustering method
Wikipedia: WPGMA
Has Member: WPGMA, Lead sectionQuote, Has Member: WPGMA, Lead section
agglomerative (bottom-up) hierarchical clustering method
DBSCAN (Wikipedia)
Has Member: DBSCAN Algorithm, LeadQuote, Has Member: DBSCAN Algorithm, Lead
Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu in 1996.
Hierarchical clustering (Wikipedia)
Has Member: Hierarchical Clustering Algorithm, LeadQuote, Has Member: Hierarchical Clustering Algorithm, Lead
In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters.
BIRCH (Wikipedia)
Has Member: BIRCH Algorithm, LeadQuote, Has Member: BIRCH Algorithm, Lead
BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets.
Wikipedia: K-means clustering
Wikimedia FoundationHas Member: K-Means Clustering Algorithm, LeadQuote, Has Member: K-Means Clustering Algorithm, Lead
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid).
Mean shift (Wikipedia)
Has Member: Mean Shift, LeadQuote, Has Member: Mean Shift, Lead
Mean shift is a non-parametric feature-space mathematical analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm.
scikit-learn: scikit-learn: Clustering
scikit-learnHas Member: Affinity Propagation Algorithm, Section 2.3.5 Affinity Propagation
AffinityPropagation creates clusters by sending messages between pairs of samples until convergence.
Has Member: Spectral Clustering Algorithm, Section 2.3.6 Spectral clustering
SpectralClustering performs a low-dimension embedding of the affinity matrix between samples, followed by clustering, e.g., by KMeans, of the components of the eigenvectors in the low dimensional space.
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