/** Resets the values for the next cluster search. */
protected void reset() {
rows = BitsUtil.ones(rowM.length);
rowcard = rowM.length;
cols = BitsUtil.ones(colM.length);
colcard = colM.length;
BitsUtil.zeroI(irow);
}
/**
* Constructor.
*
* @param rows Row dimensionality.
* @param cols Column dimensionality.
*/
protected BiclusterCandidate(int rows, int cols) {
super();
this.rows = BitsUtil.ones(rows);
this.irow = BitsUtil.zero(rows);
this.rowcard = rows;
this.rowM = new double[rows];
this.cols = BitsUtil.ones(cols);
this.colcard = cols;
this.colM = new double[cols];
}
@Override
public Clustering<BiclusterWithInversionsModel> biclustering() {
double[][] mat = RelationUtil.relationAsMatrix(relation, rowIDs);
BiclusterCandidate cand = new BiclusterCandidate(getRowDim(), getColDim());
Clustering<BiclusterWithInversionsModel> result =
new Clustering<>("Cheng-and-Church", "Cheng and Church Biclustering");
ModifiableDBIDs noise = DBIDUtil.newHashSet(relation.getDBIDs());
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Extracting Cluster", n, LOG) : null;
for (int i = 0; i < n; i++) {
cand.reset();
multipleNodeDeletion(mat, cand);
if (LOG.isVeryVerbose()) {
LOG.veryverbose(
"Residue after Alg 2: " + cand.residue + " " + cand.rowcard + "x" + cand.colcard);
}
singleNodeDeletion(mat, cand);
if (LOG.isVeryVerbose()) {
LOG.veryverbose(
"Residue after Alg 1: " + cand.residue + " " + cand.rowcard + "x" + cand.colcard);
}
nodeAddition(mat, cand);
if (LOG.isVeryVerbose()) {
LOG.veryverbose(
"Residue after Alg 3: " + cand.residue + " " + cand.rowcard + "x" + cand.colcard);
}
cand.maskMatrix(mat, dist);
BiclusterWithInversionsModel model =
new BiclusterWithInversionsModel(colsBitsetToIDs(cand.cols), rowsBitsetToIDs(cand.irow));
final ArrayDBIDs cids = rowsBitsetToIDs(cand.rows);
noise.removeDBIDs(cids);
result.addToplevelCluster(new Cluster<>(cids, model));
if (LOG.isVerbose()) {
LOG.verbose("Score of bicluster " + (i + 1) + ": " + cand.residue + "\n");
LOG.verbose("Number of rows: " + cand.rowcard + "\n");
LOG.verbose("Number of columns: " + cand.colcard + "\n");
// LOG.verbose("Total number of masked values: " + maskedVals.size() +
// "\n");
}
LOG.incrementProcessed(prog);
}
// Add a noise cluster, full-dimensional.
if (!noise.isEmpty()) {
long[] allcols = BitsUtil.ones(getColDim());
BiclusterWithInversionsModel model =
new BiclusterWithInversionsModel(colsBitsetToIDs(allcols), DBIDUtil.EMPTYDBIDS);
result.addToplevelCluster(new Cluster<>(noise, true, model));
}
LOG.ensureCompleted(prog);
return result;
}
/**
* Performs the DOC or FastDOC (as configured) algorithm on the given Database.
*
* <p>This will run exhaustively, i.e. run DOC until no clusters are found anymore / the database
* size has shrunk below the threshold for minimum cluster size.
*
* @param database Database
* @param relation Data relation
*/
public Clustering<SubspaceModel> run(Database database, Relation<V> relation) {
// Dimensionality of our set.
final int d = RelationUtil.dimensionality(relation);
// Get available DBIDs as a set we can remove items from.
ArrayModifiableDBIDs S = DBIDUtil.newArray(relation.getDBIDs());
// Precompute values as described in Figure 2.
double r = Math.abs(Math.log(d + d) / Math.log(beta * .5));
// Outer loop count.
int n = (int) (2. / alpha);
// Inner loop count.
int m = (int) (Math.pow(2. / alpha, r) * Math.log(4));
if (heuristics) {
m = Math.min(m, Math.min(1000000, d * d));
}
// Minimum size for a cluster for it to be accepted.
int minClusterSize = (int) (alpha * S.size());
// List of all clusters we found.
Clustering<SubspaceModel> result = new Clustering<>("DOC Clusters", "DOC");
// Inform the user about the number of actual clusters found so far.
IndefiniteProgress cprogress =
LOG.isVerbose() ? new IndefiniteProgress("Number of clusters", LOG) : null;
// To not only find a single cluster, we continue running until our set
// of points is empty.
while (S.size() > minClusterSize) {
Cluster<SubspaceModel> C;
if (heuristics) {
C = runFastDOC(database, relation, S, d, n, m, (int) r);
} else {
C = runDOC(database, relation, S, d, n, m, (int) r, minClusterSize);
}
if (C == null) {
// Stop trying if we couldn't find a cluster.
break;
}
// Found a cluster, remember it, remove its points from the set.
result.addToplevelCluster(C);
// Remove all points of the cluster from the set and continue.
S.removeDBIDs(C.getIDs());
if (cprogress != null) {
cprogress.setProcessed(result.getAllClusters().size(), LOG);
}
}
// Add the remainder as noise.
if (S.size() > 0) {
long[] alldims = BitsUtil.ones(d);
result.addToplevelCluster(
new Cluster<>(
S,
true,
new SubspaceModel(new Subspace(alldims), Centroid.make(relation, S).getArrayRef())));
}
LOG.setCompleted(cprogress);
return result;
}