/**
* DBSCAN-function expandCluster.
*
* <p>Border-Objects become members of the first possible cluster.
*
* @param relation Database relation to run on
* @param rangeQuery Range query to use
* @param startObjectID potential seed of a new potential cluster
* @param objprog the progress object for logging the current status
*/
protected void expandCluster(
Relation<O> relation,
RangeQuery<O> rangeQuery,
DBIDRef startObjectID,
FiniteProgress objprog,
IndefiniteProgress clusprog) {
DoubleDBIDList neighbors = rangeQuery.getRangeForDBID(startObjectID, epsilon);
ncounter += neighbors.size();
// startObject is no core-object
if (neighbors.size() < minpts) {
noise.add(startObjectID);
processedIDs.add(startObjectID);
if (objprog != null) {
objprog.incrementProcessed(LOG);
}
return;
}
ModifiableDBIDs currentCluster = DBIDUtil.newArray();
currentCluster.add(startObjectID);
processedIDs.add(startObjectID);
// try to expand the cluster
HashSetModifiableDBIDs seeds = DBIDUtil.newHashSet();
processNeighbors(neighbors.iter(), currentCluster, seeds);
DBIDVar o = DBIDUtil.newVar();
while (!seeds.isEmpty()) {
seeds.pop(o);
neighbors = rangeQuery.getRangeForDBID(o, epsilon);
ncounter += neighbors.size();
if (neighbors.size() >= minpts) {
processNeighbors(neighbors.iter(), currentCluster, seeds);
}
if (objprog != null) {
objprog.incrementProcessed(LOG);
}
}
resultList.add(currentCluster);
if (clusprog != null) {
clusprog.setProcessed(resultList.size(), LOG);
}
}
/**
* Performs a single run of FastDOC, finding a single cluster.
*
* @param database Database context
* @param relation used to get actual values for DBIDs.
* @param S The set of points we're working on.
* @param d Dimensionality of the data set we're currently working on.
* @param r Size of random samples.
* @param m Number of inner iterations (per seed point).
* @param n Number of outer iterations (seed points).
* @return a cluster, if one is found, else <code>null</code>.
*/
private Cluster<SubspaceModel> runFastDOC(
Database database, Relation<V> relation, ArrayModifiableDBIDs S, int d, int n, int m, int r) {
// Relevant attributes of highest cardinality.
long[] D = null;
// The seed point for the best dimensions.
DBIDVar dV = DBIDUtil.newVar();
// Inform the user about the progress in the current iteration.
FiniteProgress iprogress =
LOG.isVerbose()
? new FiniteProgress("Iteration progress for current cluster", m * n, LOG)
: null;
Random random = rnd.getSingleThreadedRandom();
DBIDArrayIter iter = S.iter();
outer:
for (int i = 0; i < n; ++i) {
// Pick a random seed point.
iter.seek(random.nextInt(S.size()));
for (int j = 0; j < m; ++j) {
// Choose a set of random points.
DBIDs randomSet = DBIDUtil.randomSample(S, r, random);
// Initialize cluster info.
long[] nD = BitsUtil.zero(d);
// Test each dimension.
for (int k = 0; k < d; ++k) {
if (dimensionIsRelevant(k, relation, randomSet)) {
BitsUtil.setI(nD, k);
}
}
if (D == null || BitsUtil.cardinality(nD) > BitsUtil.cardinality(D)) {
D = nD;
dV.set(iter);
if (BitsUtil.cardinality(D) >= d_zero) {
if (iprogress != null) {
iprogress.setProcessed(iprogress.getTotal(), LOG);
}
break outer;
}
}
LOG.incrementProcessed(iprogress);
}
}
LOG.ensureCompleted(iprogress);
// If no relevant dimensions were found, skip it.
if (D == null || BitsUtil.cardinality(D) == 0) {
return null;
}
// Get all points in the box.
SubspaceMaximumDistanceFunction df = new SubspaceMaximumDistanceFunction(D);
DistanceQuery<V> dq = database.getDistanceQuery(relation, df);
RangeQuery<V> rq = database.getRangeQuery(dq, DatabaseQuery.HINT_SINGLE);
// TODO: add filtering capabilities into query API!
DBIDs C = DBIDUtil.intersection(S, rq.getRangeForDBID(dV, w));
// If we have a non-empty cluster, return it.
return (C.size() > 0) ? makeCluster(relation, C, D) : null;
}