public KMeansResult cluster(double[][] centroids, double[][] instances, double threshold) {
int itr = -1;
// initialize the result
KMeansResult result = new KMeansResult();
LinkedList<Double> distortionList = new LinkedList<Double>();
result.clusterAssignment = new int[instances.length];
boolean completed = false;
// loop until finally done
while (!completed) {
// increment iteration
itr++;
// call the assignInstance method to assign the instances to centroids
assignInstance(centroids, instances, result);
// check orphaned centroid
boolean noOrphaned = false;
while (!noOrphaned) {
int orphanNum = -1;
orphanNum = checkOrphan(centroids, result);
// if the centroid is orphaned, then assign it as scenario requores
if (orphanNum >= 0) {
assignOrphan(orphanNum, centroids, instances, result);
} else {
noOrphaned = true;
}
}
// update centroids
updateCentroids(centroids, instances, result);
// compute new distortion
double distortion = calculateDistortion(centroids, instances, result);
// track the distortion
distortionList.add(distortion);
// check whether the algorithm should terminate
if (itr > 0
&& Math.abs(
(distortionList.get(itr) - distortionList.get(itr - 1))
/ distortionList.get(itr - 1))
< threshold) {
completed = true;
}
}
// set centroids to KMeansResult final centroids array
result.centroids = centroids;
// initialize result's distortion array
result.distortionIterations = new double[distortionList.size()];
// record distortionList into result of distortion array
for (int i = 0; i < distortionList.size(); i++) {
result.distortionIterations[i] = distortionList.get(i);
}
return result;
}
