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cs190c:project4c_09 [2009/04/21 14:40]
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cs190c:project4c_09 [2009/04/21 15:19] (current)
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 A good graph clustering will result in clusters of proteins with high values of similarity metrics within a cluster (which means  values based on the good edges should be large) and low values between the clusters (which means the values based on bad edges should be small). ​ A good graph clustering will result in clusters of proteins with high values of similarity metrics within a cluster (which means  values based on the good edges should be large) and low values between the clusters (which means the values based on bad edges should be small). ​
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 ====== Submission instructions ​ ====== ====== Submission instructions ​ ======
  
-The page we [[provide]] for sample graphs also contains four matrices, two for synthetic data and two for the biological data sets.  For a graph having associated matrices, use function ''​import_matrix''​ to create each graph GW, use function ''​mcl_clustering''​ to determine a clustering of the nodes in graph GC, and use graph GW to evaluate the clusters. ​  ​**Note:​ If mcl_clustering is taking too long to run on your computer, the clustering is provided for you on the page with the graphs.**+The page we [[provide]] for sample graphs also contains four matrices, two for synthetic data and two for the biological data sets.  For a graph having associated matrices, use function ''​import_matrix''​ to create each graph GW, use function ''​mcl_clustering''​ to determine a clustering of the nodes in graph GC, and use graph GW to evaluate the clusters. ​   
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 +**Note: If mcl_clustering is taking too long to run on your computer, the clustering is provided for you on the page with the graphs.**
  
 Summarizing what is to be done for each data set:  Summarizing what is to be done for each data set: 
 
cs190c/project4c_09.txt · Last modified: 2009/04/21 15:19 (external edit)
 
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