public Clustering(List<? extends Instance> points) {
HashMap<Integer, Integer> labelMap = classValues(points);
int dim = points.get(0).dataset().numAttributes() - 1;
int numClasses = labelMap.size();
int noiseLabel;
Attribute classLabel = points.get(0).dataset().classAttribute();
int lastLabelIndex = classLabel.numValues() - 1;
if (classLabel.value(lastLabelIndex) == "noise") {
noiseLabel = lastLabelIndex;
} else {
noiseLabel = -1;
}
ArrayList<Instance>[] sorted_points = (ArrayList<Instance>[]) new ArrayList[numClasses];
for (int i = 0; i < numClasses; i++) {
sorted_points[i] = new ArrayList<Instance>();
}
for (Instance point : points) {
int clusterid = (int) point.classValue();
if (clusterid == noiseLabel) continue;
sorted_points[labelMap.get(clusterid)].add((Instance) point);
}
this.clusters = new AutoExpandVector<Cluster>();
for (int i = 0; i < numClasses; i++) {
if (sorted_points[i].size() > 0) {
SphereCluster s = new SphereCluster(sorted_points[i], dim);
s.setId(sorted_points[i].get(0).classValue());
s.setGroundTruth(sorted_points[i].get(0).classValue());
clusters.add(s);
}
}
}
public Clustering(ArrayList<DataPoint> points, double overlapThreshold, int initMinPoints) {
HashMap<Integer, Integer> labelMap = Clustering.classValues(points);
int dim = points.get(0).dataset().numAttributes() - 1;
int numClasses = labelMap.size();
int num = 0;
ArrayList<DataPoint>[] sorted_points = (ArrayList<DataPoint>[]) new ArrayList[numClasses];
for (int i = 0; i < numClasses; i++) {
sorted_points[i] = new ArrayList<DataPoint>();
}
for (DataPoint point : points) {
int clusterid = (int) point.classValue();
if (clusterid == -1) continue;
sorted_points[labelMap.get(clusterid)].add(point);
num++;
}
clusters = new AutoExpandVector<Cluster>();
int microID = 0;
for (int i = 0; i < numClasses; i++) {
ArrayList<SphereCluster> microByClass = new ArrayList<SphereCluster>();
ArrayList<DataPoint> pointInCluster = new ArrayList<DataPoint>();
ArrayList<ArrayList<Instance>> pointInMicroClusters = new ArrayList();
pointInCluster.addAll(sorted_points[i]);
while (pointInCluster.size() > 0) {
ArrayList<Instance> micro_points = new ArrayList<Instance>();
for (int j = 0; j < initMinPoints && !pointInCluster.isEmpty(); j++) {
micro_points.add((Instance) pointInCluster.get(0));
pointInCluster.remove(0);
}
if (micro_points.size() > 0) {
SphereCluster s = new SphereCluster(micro_points, dim);
for (int c = 0; c < microByClass.size(); c++) {
if (((SphereCluster) microByClass.get(c)).overlapRadiusDegree(s) > overlapThreshold) {
micro_points.addAll(pointInMicroClusters.get(c));
s = new SphereCluster(micro_points, dim);
pointInMicroClusters.remove(c);
microByClass.remove(c);
// System.out.println("Removing redundant cluster based on radius overlap"+c);
}
}
for (int j = 0; j < pointInCluster.size(); j++) {
Instance instance = pointInCluster.get(j);
if (s.getInclusionProbability(instance) > 0.8) {
pointInCluster.remove(j);
micro_points.add(instance);
}
}
s.setWeight(micro_points.size());
microByClass.add(s);
pointInMicroClusters.add(micro_points);
microID++;
}
}
//
boolean changed = true;
while (changed) {
changed = false;
for (int c = 0; c < microByClass.size(); c++) {
for (int c1 = c + 1; c1 < microByClass.size(); c1++) {
double overlap = microByClass.get(c).overlapRadiusDegree(microByClass.get(c1));
// System.out.println("Overlap C"+(clustering.size()+c)+"
// ->C"+(clustering.size()+c1)+": "+overlap);
if (overlap > overlapThreshold) {
pointInMicroClusters.get(c).addAll(pointInMicroClusters.get(c1));
SphereCluster s = new SphereCluster(pointInMicroClusters.get(c), dim);
microByClass.set(c, s);
pointInMicroClusters.remove(c1);
microByClass.remove(c1);
changed = true;
break;
}
}
}
}
for (int j = 0; j < microByClass.size(); j++) {
microByClass.get(j).setGroundTruth(sorted_points[i].get(0).classValue());
clusters.add(microByClass.get(j));
}
}
for (int j = 0; j < clusters.size(); j++) {
clusters.get(j).setId(j);
}
}
