About: HCS clustering algorithm

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The HCS (Highly Connected Subgraphs) clustering algorithm (also known as the HCS algorithm, and other names such as Highly Connected Clusters/Components/Kernels) is an algorithm based on graph connectivity for cluster analysis. It works by representing the similarity data in a , and then finding all the highly connected subgraphs. It does not make any prior assumptions on the number of the clusters. This algorithm was published by Erez Hartuv and Ron Shamir in 2000.

Attributes	Values
rdf:type	Thing software
rdfs:label	HCS clustering algorithm (en)
rdfs:comment	The HCS (Highly Connected Subgraphs) clustering algorithm (also known as the HCS algorithm, and other names such as Highly Connected Clusters/Components/Kernels) is an algorithm based on graph connectivity for cluster analysis. It works by representing the similarity data in a , and then finding all the highly connected subgraphs. It does not make any prior assumptions on the number of the clusters. This algorithm was published by Erez Hartuv and Ron Shamir in 2000. (en)
rdfs:seeAlso	Distance (graph theory) Connectivity (graph theory) Minimum cut Karger's algorithm
sameAs	HCS clustering algorithm HCS clustering algorithm
dbp:wikiPageUsesTemplate	dbt:Reflist dbt:See_also dbt:Var dbt:Infobox_Algorithm
Subject	Graph algorithms
thumbnail	wiki-commons:Special:FilePath/HCS_Algorithm.gif?width=300
foaf:depiction
gold:hypernym	Algorithm
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/HCS_clustering_algorithm?oldid=1055319747&ns=0
Wikipage page ID	39226029 (xsd:integer)
page length (characters) of wiki page	8540 (xsd:nonNegativeInteger)
Wikipage revision ID	1055319747 (xsd:integer)
Link from a Wikipage to another Wikipage	Ron Shamir Graph algorithms Clique problem Cluster analysis Minimum cut Graph (abstract data type) dbr:Highly_Connected_Subgraph dbr:Similarity_graph
has abstract	The HCS (Highly Connected Subgraphs) clustering algorithm (also known as the HCS algorithm, and other names such as Highly Connected Clusters/Components/Kernels) is an algorithm based on graph connectivity for cluster analysis. It works by representing the similarity data in a , and then finding all the highly connected subgraphs. It does not make any prior assumptions on the number of the clusters. This algorithm was published by Erez Hartuv and Ron Shamir in 2000. The HCS algorithm gives a clustering solution, which is inherently meaningful in the application domain, since each solution cluster must have diameter 2 while a union of two solution clusters will have diameter 3. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/HCS_clustering_algorithm
is Link from a Wikipage to another Wikipage of	Cluster analysis HCS
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/HCS_clustering_algorithm

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