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About: Hierarchical clustering     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : yago:WikicatDataClusteringAlgorithms, within Data Space : dbpedia.demo.openlinksw.com associated with source document(s)

Type: Thingyago:WikicatClassificationAlgorithmsyago:Abstraction100002137yago:Act100030358yago:Activity100407535yago:Algorithm105847438yago:Cognition100023271yago:Datum105816622yago:Event100029378yago:Information105816287yago:Procedure101023820yago:PsychologicalFeature100023100yago:YagoPermanentlyLocatedEntityyago:Rule105846932yago:WikicatDataClusteringAlgorithms New Facet based on Instances of this Class

In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories: * Agglomerative: This is a "bottom-up" approach: Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. * Divisive: This is a "top-down" approach: All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.