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  2. HCS clustering algorithm - Wikipedia

    en.wikipedia.org/wiki/HCS_clustering_algorithm

    Therefore, the number of edges in a highly connected graph must be at least (n × n/2)/2, where we sum the degrees of each vertex and divide by 2. (b) By definition, each iteration removes a minimum cut with <= n/2 edges. Theorems 1 and 2a provide a strong indication of a final cluster's homogeneity.

  3. Multidimensional scaling - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_scaling

    It is also known as Principal Coordinates Analysis (PCoA), Torgerson Scaling or Torgerson–Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain, [2] which is given by (,,...,) = (, (),) /, where denote vectors in N-dimensional space, denotes the scalar product between ...

  4. t-distributed stochastic neighbor embedding - Wikipedia

    en.wikipedia.org/wiki/T-distributed_stochastic...

    It is based on Stochastic Neighbor Embedding originally developed by Geoffrey Hinton and Sam Roweis, [1] where Laurens van der Maaten and Hinton proposed the t-distributed variant. [2] It is a nonlinear dimensionality reduction technique for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions.

  5. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters).

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

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

    The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]

  7. CURE algorithm - Wikipedia

    en.wikipedia.org/wiki/CURE_algorithm

    To avoid the problems with non-uniform sized or shaped clusters, CURE employs a hierarchical clustering algorithm that adopts a middle ground between the centroid based and all point extremes. In CURE, a constant number c of well scattered points of a cluster are chosen and they are shrunk towards the centroid of the cluster by a fraction α.

  8. 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

  9. Biclustering - Wikipedia

    en.wikipedia.org/wiki/Biclustering

    Biclustering, block clustering, [1] [2] Co-clustering or two-mode clustering [3] [4] [5] is a data mining technique which allows simultaneous clustering of the rows and columns of a matrix. The term was first introduced by Boris Mirkin [ 6 ] to name a technique introduced many years earlier, [ 6 ] in 1972, by John A. Hartigan .