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A cluster in general is a group or bunch of several discrete items that are close to each other. The cluster diagram figures a cluster, such as a network diagram figures a network, a flow diagram a process or movement of objects, and a tree diagram an abstract tree. But all these diagrams can be considered interconnected: A network diagram can ...
Cluster analysis is used to describe and to make spatial and temporal comparisons of communities (assemblages) of organisms in heterogeneous environments. It is also used in plant systematics to generate artificial phylogenies or clusters of organisms (individuals) at the species, genus or higher level that share a number of attributes.
Every cluster graph is a block graph, a cograph, and a claw-free graph. [1] Every maximal independent set in a cluster graph chooses a single vertex from each cluster, so the size of such a set always equals the number of clusters; because all maximal independent sets have the same size, cluster graphs are well-covered.
In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. The method consists of plotting the explained variation as a function of the number of clusters and picking the elbow of the curve as the number of clusters to use.
The most common size for an HA cluster is a two-node cluster, since that is the minimum required to provide redundancy, but many clusters consist of many more, sometimes dozens of nodes. The attached diagram is a good overview of a classic HA cluster, with the caveat that it does not make any mention of quorum/witness functionality (see above).
Conceptual clustering is obviously closely related to data clustering; however, in conceptual clustering it is not only the inherent structure of the data that drives cluster formation, but also the Description language which is available to the learner.
A dendrogram is a diagram representing a tree. This diagrammatic representation is frequently used in different contexts: in hierarchical clustering, it illustrates the arrangement of the clusters produced by the corresponding analyses. [4] in computational biology, it shows the clustering of genes or samples, sometimes in the margins of ...
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
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