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  2. Hierarchical clustering - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_clustering

    The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of () and requires () memory, which makes it too slow for even medium data sets. . However, for some special cases, optimal efficient agglomerative methods (of complexity ()) are known: SLINK [2] for single-linkage and CLINK [3] for complete-linkage clusteri

  3. Ward's method - Wikipedia

    en.wikipedia.org/wiki/Ward's_method

    In statistics, Ward's method is a criterion applied in hierarchical cluster analysis. Ward's minimum variance method is a special case of the objective function approach originally presented by Joe H. Ward, Jr. [ 1 ] Ward suggested a general agglomerative hierarchical clustering procedure, where the criterion for choosing the pair of clusters ...

  4. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Cluster analysis is for example used to identify groups of schools or students with similar properties. Typologies From poll data, projects such as those undertaken by the Pew Research Center use cluster analysis to discern typologies of opinions, habits, and demographics that may be useful in politics and marketing.

  5. Determining the number of clusters in a data set - Wikipedia

    en.wikipedia.org/wiki/Determining_the_number_of...

    In statistics and data mining, X-means clustering is a variation of k-means clustering that refines cluster assignments by repeatedly attempting subdivision, and keeping the best resulting splits, until a criterion such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC) is reached.

  6. Dendrogram - Wikipedia

    en.wikipedia.org/wiki/Dendrogram

    For a clustering example, suppose that five taxa (to ) have been clustered by UPGMA based on a matrix of genetic distances.The hierarchical clustering dendrogram would show a column of five nodes representing the initial data (here individual taxa), and the remaining nodes represent the clusters to which the data belong, with the arrows representing the distance (dissimilarity).

  7. Single-linkage clustering - Wikipedia

    en.wikipedia.org/wiki/Single-linkage_clustering

    In statistics, single-linkage clustering is one of several methods of hierarchical clustering.It is based on grouping clusters in bottom-up fashion (agglomerative clustering), at each step combining two clusters that contain the closest pair of elements not yet belonging to the same cluster as each other.

  8. Nearest-neighbor chain algorithm - Wikipedia

    en.wikipedia.org/wiki/Nearest-neighbor_chain...

    In the theory of cluster analysis, the nearest-neighbor chain algorithm is an algorithm that can speed up several methods for agglomerative hierarchical clustering.These are methods that take a collection of points as input, and create a hierarchy of clusters of points by repeatedly merging pairs of smaller clusters to form larger clusters.

  9. UPGMA - Wikipedia

    en.wikipedia.org/wiki/UPGMA

    UPGMA (unweighted pair group method with arithmetic mean) is a simple agglomerative (bottom-up) hierarchical clustering method. It also has a weighted variant, WPGMA, and they are generally attributed to Sokal and Michener.