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Article: Clustering in GlassFish Version 3.1

 
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Old 03-14-2011
Article: Clustering in GlassFish Version 3.1

This java.net article describes the clustering capabilities of GlassFish version 3.1 and helps you get started deploying your application to a GlassFish cluster.

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CLUSTER(1)						      General Commands Manual							CLUSTER(1)

NAME
cluster - find clusters in a graph and augment the graph with this information. SYNOPSIS
cluster [-v?] [ -Ck ] [ -ck ] [ -o outfile ] [ files ] DESCRIPTION
cluster takes as input a graph in DOT format, finds node clusters and augments the graph with this information. The clusters are specified by the "cluster" attribute attached to nodes; cluster values are non-negative integers. cluster attempts to maximize the modularity of the clustering. If the edge attribute "weight" is defined, this will be used in computing the clustering. OPTIONS
The following options are supported: -Ck specifies a targeted number of clusters that should be generated. The specified number k is only a suggestion and may not be real- isable. If k == 0, the default, the number of clusters that approximately optimizes the modularity is returned. -ck specifies clustering method. If k == 0, the default, the modularity will be used. If k == 1 modularity quality will be used. -ooutfile Specifies that output should go into the file outfile. By default, stdout is used. -v Verbose mode. EXAMPLES
Applying cluster to the following graph, graph { 1--2 [weight=10.] 2--3 [weight=1] 3--4 [weight=10.] 4--5 [weight=10] 5--6 [weight=10] 3--6 [weight=0.1] 4--6 [weight=10.] } gives graph { node [cluster="-1"]; 1 [cluster=1]; 2 [cluster=1]; 3 [cluster=2]; 4 [cluster=2]; 5 [cluster=2]; 6 [cluster=2]; 1 -- 2 [weight="10."]; 2 -- 3 [weight=1]; 3 -- 4 [weight="10."]; 4 -- 5 [weight=10]; 5 -- 6 [weight=10]; 3 -- 6 [weight="0.1"]; 4 -- 6 [weight="10."]; } AUTHOR
Yifan Hu <yifanhu@research.att.com> SEE ALSO
gvmap(1) Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment (2008), P10008. 3 March 2011 CLUSTER(1)