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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).
Methods have been developed to improve and automate existing hierarchical clustering algorithms [5] such as an automated version of single linkage hierarchical cluster analysis (HCA). This computerized method bases its success on a self-consistent outlier reduction approach followed by the building of a descriptive function which permits ...
Different Gaussian model-based clustering methods have been developed with an eye to handling high-dimensional data. These include the pgmm method, [11] which is based on the mixture of factor analyzers model, and the HDclassif method, based on the idea of subspace clustering. [12]
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
Unlike many previous methods, the gap statistics can tell us that there is no value of k for which there is a good clustering, but the reliability depends on how plausible the assumed null distribution (e.g., a uniform distribution) is on the given data. This tends to work well in synthetic settings, but cannot handle difficult data sets with ...
This category contains algorithms used for cluster analysis. Pages in category "Cluster analysis algorithms" The following 42 pages are in this category, out of 42 total.
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.
Different methods for correlation clustering of this type are discussed in [13] and the relationship to different types of clustering is discussed in. [14] See also Clustering high-dimensional data. Correlation clustering (according to this definition) can be shown to be closely related to biclustering. As in biclustering, the goal is to ...