Decision boundary for label vote vectors computed by SVM.

<p>In the above scatter plot, each point corresponds to the number of true/false votes accumulated by each substructure across all clusterings. Combining the above label vote vectors with the known labels for substructures to train an svm (using linear kernel) results in the decision boundary shown as the bold black line. The red and blue regions (right and left sides of the boundary, respectively) denote the values for which the predicted label will be <b>false</b> and <b>true</b>, respectively. Blue points indicate substructures known to have the <b>true</b> label while red points denote the <b>false</b> label. In the case of Roscovitine above, wide separation between the two classes exists.