04-04-2012
Raspberry Pi anyone?
Anyone order or get the new Rasberry Pi? I'm still waiting. Even though it's a fun project, I think these will turn out to be toys for people who already have PC's, and a boon to those who do not.
But that is not stopping me. If my wallet holds out, maybe I can build a Raspberry Pi beowulf cluster on a small scale.
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LEARN ABOUT CENTOS
cluster
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)