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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

    Ward's minimum variance method can be defined and implemented recursively by a Lance–Williams algorithm. The Lance–Williams algorithms are an infinite family of agglomerative hierarchical clustering algorithms which are represented by a recursive formula for updating cluster distances at each step (each time a pair of clusters is merged).

  4. Hierarchical clustering of networks - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_clustering_of...

    For this reason, their use in hierarchical clustering techniques is far from optimal. [1] Edge betweenness centrality has been used successfully as a weight in the Girvan–Newman algorithm. [1] This technique is similar to a divisive hierarchical clustering algorithm, except the weights are recalculated with each step.

  5. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Ideas from density-based clustering methods (in particular the DBSCAN/OPTICS family of algorithms) have been adapted to subspace clustering (HiSC, [25] hierarchical subspace clustering and DiSH [26]) and correlation clustering (HiCO, [27] hierarchical correlation clustering, 4C [28] using "correlation connectivity" and ERiC [29] exploring ...

  6. 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.

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

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

    Other algorithms such as DBSCAN and OPTICS algorithm do not require the specification of this parameter; hierarchical clustering avoids the problem altogether. The correct choice of k is often ambiguous, with interpretations depending on the shape and scale of the distribution of points in a data set and the desired clustering resolution of the ...

  8. Single-linkage clustering - Wikipedia

    en.wikipedia.org/wiki/Single-linkage_clustering

    In the naive algorithm for agglomerative clustering, implementing a different linkage scheme may be accomplished simply by using a different formula to calculate inter-cluster distances in the algorithm. The formula that should be adjusted has been highlighted using bold text in the above algorithm description.

  9. Complete-linkage clustering - Wikipedia

    en.wikipedia.org/wiki/Complete-linkage_clustering

    Alternative linkage schemes include single linkage clustering and average linkage clustering - implementing a different linkage in the naive algorithm is simply a matter of using a different formula to calculate inter-cluster distances in the initial computation of the proximity matrix and in step 4 of the above algorithm. An optimally ...