diff --git a/SS_TestOpsOnTSClusters.m b/SS_TestOpsOnTSClusters.m
index dd23b50..9909f5b 100644
--- a/SS_TestOpsOnTSClusters.m
+++ b/SS_TestOpsOnTSClusters.m
@@ -3,37 +3,31 @@
 load('run_options.mat');
 load('HCTSA_N.mat');
 load('linkage_clustered_ops.mat');
-load('resid_variance.mat');
+load('auto_chosen_ops.mat');
+
+% Create a reduced data matrix using the automatically chosen operations
+reducedMat = TS_DataMat(:,autoChosenIdxs);
+reducedDists = pdist(reducedMat);
 
 % Cluster the time series using the reduced (K-medoids) operation space
 maxIter = 100;
-nrepeats = 500;
 ts_k = round(size(TS_DataMat,1) / av_ts_cluster_size);
 
-[CCi, Cass, err, Cord] = BF_kmedoids(squareform(S_red), ts_k, maxIter, nrepeats); 
+[~,Cass,~,Cord] = BF_kmedoids(squareform(reducedDists), ts_k, maxIter, ts_km_repeats); 
     
 % Sort clusters by size
 [~,I] = sort(cellfun(@length,Cord),'descend');
 
-% Plot a few TS from each of the clusters 
-sPlotNum = 1;
-plotsPerGroup = 4;
-plotColours = get(0,'DefaultAxesColorOrder');
-numGroups = 10;
-for i = 1:numGroups    
+figure;
+plotOptions	= struct('plotFreeForm',1,'displayTitles',0,'newFigure',0); 
+numPerGroup = 40;
+maxLength = 500;
+numPlots = 12;
+for i = 1:numPlots
+    subplot(2,numPlots / 2,i);
     series = find(Cass == I(i));
-    for j = 1:min(plotsPerGroup,length(series))
-        subplot(numGroups,plotsPerGroup,sPlotNum);
-        plot(TimeSeries(series(j)).Data,'color',plotColours(max(1,mod(i,8)),:));
-        set(gca,'XTickLabel','','YTickLabel','')
-        if j == 1
-            title(['Group ',num2str(i)]);
-        end
-        title(TimeSeries(series(j)).Keywords);
-        sPlotNum = sPlotNum + 1;
-    end
-    sPlotNum = (plotsPerGroup * i) + 1;
-end
 
+    TS_plot_timeseries('norm',numPerGroup,series,maxLength,plotOptions);
+    title(sprintf('Cluster %i (%i time series)',i,length(series)));
 end