7:00 – 12:00 Research

• Biting the bullet on Dynamic Time Warping as a way of identifying cluster members. Still not sure why a least squares approach isn’t a standard approach.
  • This post seems to be helpful: stats.stackexchange.com/questions/131281/dynamic-time-warping-clustering
  • FastDTW (java)
  • The JavaML library: java-ml.sourceforge.net
  • Well, that seems pretty straightforward. I put the full folder in my svn so I don’t have to deal with Sourceforge’s ads.

import net.sf.javaml.distance.fastdtw.dtw.FastDTW;
import net.sf.javaml.distance.fastdtw.timeseries.TimeSeries;
import net.sf.javaml.distance.fastdtw.timeseries.TimeSeriesPoint;

TimeSeries tsI = new TimeSeries(1);
TimeSeries tsJ = new TimeSeries(1);

TimeSeriesPoint tspI;
TimeSeriesPoint tspJ;

double t = 0;
double offset = 0.0;
double amplitude = 2.0;
double step = 0.1;
while(t < 10) {
    double[] v1 = {Math.sin(t)};
    double[] v2 = {Math.sin(t+offset)*amplitude};
    tspI = new TimeSeriesPoint(v1);
    tspJ = new TimeSeriesPoint(v2);
    tsI.addLast(t, tspI);
    tsJ.addLast(t, tspJ);

    t += step;
}

System.out.println("FastDTW.getWarpDistBetween(tsI, tsJ) = "+FastDTW.getWarpDistBetween(tsI, tsJ));

FastDTW.getWarpDistBetween(tsI, tsJ) = 46.33334518229166

• Note that the measure can be through all of the dimensions, so this may take some refactoring
• Next step is to add this to the FlockRecorder class and output to excel and ARFF. I think this should replace the ‘deltas’ outputs. Done!
• Running DBSCAN clustering in WEKA on the outputs
  • All Exploit – Social Radius = 0: All NOISE
  • All Exploit – Social Radius = 0.1 ALL NOISE
  • All Exploit – Social Radius = 0.2 (32 NOISE)

    === Model and evaluation on training set ===
    
    Clustered Instances
    
    0       68 (100%)
    
    Unclustered instances : 32
    
    Class attribute: AgentBias_
    Classes to Clusters:
    
      0  -- assigned to cluster
     68 | EXPLOITER
    
    Cluster 0 -- EXPLOITER
    
    Incorrectly clustered instances :	0.0	  0      %

  • All Exploit – Social Radius = 0.4 (86 NOISE)

    == Model and evaluation on training set ===
    
    Clustered Instances
    
    0       14 (100%)
    
    Unclustered instances : 86
    
    Class attribute: AgentBias_
    Classes to Clusters:
    
      0  -- assigned to cluster
     14 | EXPLOITER
    
    Cluster 0 -- EXPLOITER
    
    Incorrectly clustered instances :	0.0	  0      %

  • All Exploit – Social Radius = 0.8 (41 NOISE)

    === Model and evaluation on training set ===
    
    Clustered Instances
    
    0       45 ( 76%)
    1        7 ( 12%)
    2        7 ( 12%)
    
    Unclustered instances : 41
    
    Class attribute: AgentBias_
    Classes to Clusters:
    
      0  1  2  -- assigned to cluster
     45  7  7 | EXPLOITER
    
    Cluster 0 -- EXPLOITER
    Cluster 1 -- No class
    Cluster 2 -- No class
    
    Incorrectly clustered instances :	14.0	 14      %

  • All Exploit – Social Radius = 1.6 (51 NOISE)

    === Model and evaluation on training set ===
    
    Clustered Instances
    
    0       49 (100%)
    
    Unclustered instances : 51
    
    Class attribute: AgentBias_
    Classes to Clusters:
    
      0  -- assigned to cluster
     49 | EXPLOITER
    
    Cluster 0 -- EXPLOITER
    
    Incorrectly clustered instances :	0.0	  0      %

  • All Exploit – Social Radius = 3.2 (9 NOISE)

    === Model and evaluation on training set ===
    
    Clustered Instances 
    
    0       91 (100%)
    
    Unclustered instances : 9
    
    Class attribute: AgentBias_
    Classes to Clusters:
    
      0  -- assigned to cluster
     91 | EXPLOITER
    
    Cluster 0 -- EXPLOITER
    
    Incorrectly clustered instances :	0.0	  0      %

  • All Exploit – Social Radius = 6.4 (8 NOISE)

    === Model and evaluation on training set ===
    
    Clustered Instances
    
    0       86 ( 93%)
    1        6 (  7%)
    
    Unclustered instances : 8
    
    Class attribute: AgentBias_
    Classes to Clusters:
    
      0  1  -- assigned to cluster
     86  6 | EXPLOITER
    
    Cluster 0 -- EXPLOITER
    Cluster 1 -- No class
    
    Incorrectly clustered instances :	6.0	  6      %
    

  • All Exploit – Social Radius = 10

    === Model and evaluation on training set ===
    
    Clustered Instances
    
    0       82 ( 91%)
    1        8 (  9%)
    
    Unclustered instances : 10
    
    Class attribute: AgentBias_
    Classes to Clusters:
    
      0  1  -- assigned to cluster
     82  8 | EXPLOITER
    
    Cluster 0 -- EXPLOITER
    Cluster 1 -- No class
    
    Incorrectly clustered instances :	8.0	  8      %

• So what this all means is that the DTW produces reasonable data that can be used for clustering. The results seem to match the plots. I think I can write this up now…

12:00 – 5:00 BRC

• Clustering discussions with Aaron
• GEM Meeting