文件列表:

rnsc\definitions.h (117, 2009-06-11)
rnsc\linkedList3.cpp (6444, 2009-06-11)
rnsc\experiment.cpp~ (8631, 2004-10-18)
rnsc\graph2.cpp (12603, 2009-06-11)
rnsc\graph3.cpp~ (13000, 2004-10-18)
rnsc\graph5.cpp (15636, 2009-06-11)
rnsc\rnsc (64405, 2009-06-13)
rnsc\graph.cpp (15211, 2009-06-11)
rnsc\linkedList2.cpp (3974, 2009-06-11)
rnsc\out.rnsc (62, 2009-06-13)
rnsc\linkedList.h~ (2525, 2009-06-11)
rnsc\build (232, 2009-06-13)
rnsc\statsBook.h (430, 2009-06-11)
rnsc\graph3.cpp (11761, 2009-06-11)
rnsc\graph2.cpp~ (13842, 2004-10-18)
rnsc\graph4.cpp (14593, 2009-06-11)
rnsc\graph4.cpp~ (15833, 2004-10-18)
rnsc\printing.cpp~ (3919, 2004-10-18)
rnsc\linkedList2.cpp~ (5212, 2004-10-18)
rnsc\main.cpp~ (6750, 2004-10-18)
rnsc\graph (105, 2004-10-18)
rnsc\linkedList.cpp (4130, 2009-06-11)
rnsc\linkedList4.c (4111, 2009-06-11)
rnsc\linkedList4.c~ (5349, 2009-03-18)
rnsc\graph.h~ (6804, 2009-03-18)
rnsc\graph.cpp~ (16450, 2004-10-18)
rnsc\miscFunctions.cpp (2327, 2009-06-11)
rnsc\miscFunctions.h (243, 2009-06-11)
rnsc\printing.cpp (2681, 2009-06-11)
rnsc\experiment.cpp (7392, 2009-06-11)
rnsc\linkedList.cpp~ (5369, 2004-10-18)
rnsc\miscFunctions.cpp~ (3565, 2004-10-18)
rnsc\experiment.h (1321, 2009-06-11)
rnsc\statsBook.h~ (1668, 2009-03-18)
rnsc\linkedList3.cpp~ (7682, 2004-10-18)
rnsc\graph6.cpp~ (5641, 2004-10-18)
rnsc\graph6.cpp (4402, 2009-06-11)
rnsc\y2k.command (175, 2004-10-18)
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NAME rnsc - Restricted Neighbourhood Search Cluster Algorithm SYNOPSIS rnsc -g graph_filename [-s] [-cnum] [-tnum] [-Tnum] [-nnum] [-Nnum] [-enum] [-Dnum] [-dnum] [-i input_clustering_filename] [-o output_clustering_filename] DESCRIPTION The RNSC algorithm takes a simple graph as input and clusters it, writing the final clustering to output_clustering_filename. There are several user-input parameters that affect the algorithm's behaviour. OPTIONS -g graph_filename RNSC reads the input graph from a filename, given as graph_filename. The default filename is "graph". This file must have a very strict format. The format is an adjacency list in which each edge appears only once. The vertices are labelled with the integers 0, 1, ..., n-1. The list of neighbours for vertex v appears as v n_1 n_2 ... n_x -1 As an example, an instance of C_5, the cycle on four vertices, would appear as 0 1 4 -1 1 2 -1 2 3 -1 3 4 -1 4 -1 -s Skip the naive scheme and proceed directly to the scaled scheme in each experiment. This option generally causes the algorithm to take longer, but it is recommended when using an input clustering from a file. -c num Allow no more than "num" clusters. "num" must be between 2 and n, where n is the number of vertices in the graph. If the -c option is not specified or an invalid value is given, n clusters are used. -t num Set the tabu length to "num". Default value is 1. Note that when the -T option is used, vertices can appear on the tabu list more than once and moving them is only forbidden when they are on the tabu list more than TabuTol times, where TabuTol is the tabu list tolerance. -T num Set the tabu list tolerance to "num". Default value is 1. The tabu list tolerance is the number of times a vertex must appear on the tabu list before moving it is forbidden. -n num Set the naive stopping tolerance to "num". Default value is 5. This is the number of steps that the naive scheme will continue without improving the best cost. If you run the scaled scheme, using a higher naive stopping tolerance isn't likely to improve your results. -N num Set the scaled stopping tolerance to "num". Default value is 5. This is the number of steps that the scaled scheme will continue without improving the best cost. Setting the tolerance to 0 will cause the algorithm to skip the scaled scheme. -e num Run "num" experiments. The best final clustering over all experiments will be written to file. Default is 1. -D num Set the diversification frequency to "num". Without this option, no diversification will be performed. If the -d flag is also used, then "num" is the shuffling diversification frequency. If the -d flag is not used, then "num" is the destructive diversification frequency. It is recommended that the -d flag is used, because in the fast version of RNSC (i.e. this one), destructive diversification isn't much help. -d num Set the shuffling diversification length to "num". That means that the last "num" moves in the diversification period will be diversification moves. Don't set this to be higher than the diversification frequency. -i input_clustering_filename Read an initial clustering from file. This file must have the same format as an RNSC output clustering file. That is, each cluster is a list of vertices, terminated with a -1 token. By default, the initial clustering is random. -o output_clustering_filename Write the final clustering to a given filename. Default is out.rnsc. EXAMPLE rnsc -g ../graphs/11.gra -e3 -iin.rnsc -oout.rnsc -n2 -N100 -D40 -d10 -c300 -t15 -T2 SEE ALSO Andrew D. King and Rudi Mathon. A fast cost-based graph clustering algorithm. Manuscript. Andrew D. King. Graph Clustering with Restricted Neighbourhood Search. M. Sc. thesis, University of Toronto. 2004. Andrew D. King, Natasa Przulj, and Igor Jurisica. Protein complex prediction via cost-based clustering. Bioinformatics Advance Access. 2004.

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