/**
* 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;
}
/**
* Performs a single run of DOC, 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).
* @param minClusterSize Minimum size a cluster must have to be accepted.
* @return a cluster, if one is found, else <code>null</code>.
*/
private Cluster<SubspaceModel> runDOC(
Database database,
Relation<V> relation,
ArrayModifiableDBIDs S,
final int d,
int n,
int m,
int r,
int minClusterSize) {
// Best cluster for the current run.
DBIDs C = null;
// Relevant attributes for the best cluster.
long[] D = null;
// Quality of the best cluster.
double quality = Double.NEGATIVE_INFINITY;
// Bounds for our cluster.
// ModifiableHyperBoundingBox bounds = new ModifiableHyperBoundingBox(new
// double[d], new double[d]);
// Weights for distance (= rectangle query)
SubspaceMaximumDistanceFunction df = new SubspaceMaximumDistanceFunction(BitsUtil.zero(d));
DistanceQuery<V> dq = database.getDistanceQuery(relation, df);
RangeQuery<V> rq = database.getRangeQuery(dq);
// 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();
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 and build bounding box.
for (int k = 0; k < d; ++k) {
if (dimensionIsRelevant(k, relation, randomSet)) {
BitsUtil.setI(nD, k);
}
}
if (BitsUtil.cardinality(nD) > 0) {
// Get all points in the box.
df.setSelectedDimensions(nD);
// TODO: add filtering capabilities into query API!
DBIDs nC = DBIDUtil.intersection(S, rq.getRangeForDBID(iter, w));
if (LOG.isDebuggingFiner()) {
LOG.finer(
"Testing a cluster candidate, |C| = "
+ nC.size()
+ ", |D| = "
+ BitsUtil.cardinality(nD));
}
// Is the cluster large enough?
if (nC.size() < minClusterSize) {
// Too small.
if (LOG.isDebuggingFiner()) {
LOG.finer("... but it's too small.");
}
} else {
// Better cluster than before?
double nQuality = computeClusterQuality(nC.size(), BitsUtil.cardinality(nD));
if (nQuality > quality) {
if (LOG.isDebuggingFiner()) {
LOG.finer("... and it's the best so far: " + nQuality + " vs. " + quality);
}
C = nC;
D = nD;
quality = nQuality;
} else {
if (LOG.isDebuggingFiner()) {
LOG.finer("... but we already have a better one.");
}
}
}
}
LOG.incrementProcessed(iprogress);
}
}
LOG.ensureCompleted(iprogress);
return (C != null) ? makeCluster(relation, C, D) : null;
}
