@Override
public Clustering<E, C> reduce(
Clustering[] clusts, Algorithm<E, C> alg, ColorGenerator cg, Props props) {
Graph graph = createGraph(clusts);
// degree of freedom
double df;
double w, attain;
EdgeIterable neigh;
PriorityQueue<DoubleElem> pq = new PriorityQueue<>(graph.getNodeCount());
DoubleElem<Node> elem;
// for each node compute attainment score
for (Node node : graph.getNodes()) {
neigh = graph.getEdges(node);
df = neigh.size();
w = 0.0;
for (Edge ne : neigh) {
w += ne.getWeight();
}
attain = w / df;
elem = new DoubleElem<>(node, attain);
pq.add(elem);
}
// number of clusters is just a hint
int k = props.getInt(KMeans.K, 5);
double relax = props.getDouble(RELAX, 0.5);
Clustering<E, C> result = new ClusterList(k);
Dataset<? extends Instance> dataset = clusts[0].getLookup().lookup(Dataset.class);
result.lookupAdd(dataset);
ObjectOpenHashSet<Node> blacklist = new ObjectOpenHashSet();
Node node, other;
Cluster curr;
double maxW;
while (!pq.isEmpty()) {
elem = pq.poll();
node = elem.getElem();
if (!blacklist.contains(node)) {
blacklist.add(node);
curr = result.createCluster();
if (cg != null) {
curr.setColor(cg.next());
}
curr.add(node.getInstance());
EdgeIterable iter = graph.getEdges(node);
maxW = -1;
for (Edge ne : iter) {
if (ne.getWeight() > maxW) {
maxW = ne.getWeight();
}
}
// add immediate neighbours with max weight to same cluster
if (maxW >= 0.0) {
for (Edge ne : iter) {
// when relax set to 0.0, only items with maximum weight
// will be added to the same cluster
w = ne.getWeight() + relax * ne.getWeight();
if (w >= maxW) {
if (!node.equals(ne.getSource())) {
other = ne.getSource();
} else {
other = ne.getTarget();
}
if (!blacklist.contains(other)) {
curr.add(other.getInstance());
blacklist.add(other);
}
}
}
}
}
}
// TODO merge some clusters
return result;
}
