docs: public double evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) {
      boolean square = !distance.isSquared();
      int ignorednoise = 0;
  
      List<? extends Cluster<?>> clusters = c.getAllClusters();
      double ssq = 0, sum = 0;
      for(Cluster<?> cluster : clusters) {
        if(cluster.size() <= 1 || cluster.isNoise()) {
          switch(noiseOption){
          case IGNORE_NOISE:
            ignorednoise += cluster.size();
            continue;
          case TREAT_NOISE_AS_SINGLETONS:
            continue;
          case MERGE_NOISE:
            break; // Treat as cluster below:
          }
        }
        NumberVector center = ModelUtil.getPrototypeOrCentroid(cluster.getModel(), rel, cluster.getIDs());
        for(DBIDIter it1 = cluster.getIDs().iter(); it1.valid(); it1.advance()) {
          final double d = distance.distance(center, rel.get(it1));
          sum += d;
          ssq += square ? d * d : d;
        }
      }
      final int div = Math.max(1, rel.size() - ignorednoise);
      if(LOG.isStatistics()) {
        LOG.statistics(new DoubleStatistic(key + ".mean", sum / div));
        LOG.statistics(new DoubleStatistic(key + ".ssq", ssq));
        LOG.statistics(new DoubleStatistic(key + ".rmsd", FastMath.sqrt(ssq / div)));
      }
  
      EvaluationResult ev = EvaluationResult.findOrCreate(db.getHierarchy(), c, "Internal Clustering Evaluation", "internal evaluation");
      MeasurementGroup g = ev.findOrCreateGroup("Distance-based Evaluation");
      g.addMeasure("Mean distance", sum / div, 0., Double.POSITIVE_INFINITY, true);
      g.addMeasure("Sum of Squares", ssq, 0., Double.POSITIVE_INFINITY, true);
      g.addMeasure("RMSD", FastMath.sqrt(ssq / div), 0., Double.POSITIVE_INFINITY, true);
      db.getHierarchy().add(c, ev);
      return ssq;
    }