Nonconvex Clusters

Introduction to Clustering Procedures

Nonconvex Clusters

If the population clusters have very different covariance matrices, using PROC ACECLUS is of no avail. Although methods exist for estimating multinormal clusters with unequal covariance matrices (Wolfe 1970; Symons 1981; Everitt and Hand 1981; Titterington, Smith, and Makov 1985; McLachlan and Basford 1988, these methods tend to have serious problems with initialization and may converge to degenerate solutions. For unequal covariance matrices or radically nonnormal distributions, the best approach to cluster analysis is through nonparametric density estimation, as in density linkage. The next example illustrates population clusters with nonconvex density contours. The following SAS statements produce Figure 8.22.

   data noncon;
      keep x y;
      do i=1 to 100;
         a=i*.0628319;
         x=cos(a)+(i>50)+rannor(7)*.1;
         y=sin(a)+(i>50)*.3+rannor(7)*.1;
         output;
      end;
   run;

   proc fastclus data=noncon out=out maxc=2 noprint;
   run;

   proc gplot;
      plot y*x=cluster/frame cframe=ligr 
           vaxis=axis1 haxis=axis2 legend=legend1;
      title 'FASTCLUS Analysis';
      title2 'of Data Containing Nonconvex Clusters';
   run;

Figure 8.22: Data Containing Nonconvex Clusters: PROC FASTCLUS

The following SAS statements produce Figure 8.23.

   proc cluster data=noncon outtree=tree 
                method=centroid noprint;
   run;

   proc tree noprint out=out n=2 dock=5;
      copy x y;
   run;

   proc gplot;
      plot y*x=cluster/frame cframe=ligr 
           vaxis=axis1 haxis=axis2 legend=legend1;
      title 'Centroid Cluster Analysis';
      title2 'of Data Containing Nonconvex Clusters';
   run;

Figure 8.23: Data Containing Nonconvex Clusters: PROC CLUSTER with METHOD=CENTROID

The following SAS statements produce Figure 8.24.

   proc cluster data=noncon outtree=tree 
                method=twostage k=10 noprint;
   run;

   proc tree noprint out=out n=2;
      copy x y;
   run;

   proc gplot;
      plot y*x=cluster/frame cframe=ligr 
           vaxis=axis1 haxis=axis2 legend=legend1;
      title 'Two-Stage Density Linkage Cluster Analysis';
      title2 'of Data Containing Nonconvex Clusters';
   run;

Figure 8.24: Data Containing Nonconvex Clusters: PROC CLUSTER with METHOD=TWOSTAGE

Ward's method and average linkage, not shown, do better than PROC FASTCLUS but not as well as the centroid method. Two-stage density linkage recovers the correct clusters, as does single linkage, which is not shown.

The preceding examples are intended merely to illustrate some of the properties of clustering methods in common use. If you intend to perform a cluster analysis, you should consult more systematic and rigorous studies of the properties of clustering methods, such as Milligan (1980).

Chapter Contents
Previous
Next
Top