@Override
protected void prepareComplete() {
StringBuilder buf = LOG.isDebuggingFine() ? new StringBuilder() : null;
scalingreferencevalues = new double[dimensionality];
randomPerAttribute = new Random[dimensionality];
if (scalingreference == ScalingReference.STDDEV) {
if (buf != null) {
buf.append("Standard deviation per attribute: ");
}
for (int d = 0; d < dimensionality; d++) {
scalingreferencevalues[d] = mvs[d].getSampleStddev() * percentage;
if (scalingreferencevalues[d] == 0 || Double.isNaN(scalingreferencevalues[d])) {
scalingreferencevalues[d] = percentage;
}
randomPerAttribute[d] = new Random(RANDOM.nextLong());
if (buf != null) {
buf.append(" ").append(d).append(": ").append(scalingreferencevalues[d] / percentage);
}
}
} else if (scalingreference == ScalingReference.MINMAX
&& minima.length == 0
&& maxima.length == 0) {
if (buf != null) {
buf.append("extension per attribute: ");
}
for (int d = 0; d < dimensionality; d++) {
scalingreferencevalues[d] = (mvs[d].getMax() - mvs[d].getMin()) * percentage;
if (scalingreferencevalues[d] == 0 || Double.isNaN(scalingreferencevalues[d])) {
scalingreferencevalues[d] = percentage;
}
randomPerAttribute[d] = new Random(RANDOM.nextLong());
if (buf != null) {
buf.append(" ").append(d).append(": ").append(scalingreferencevalues[d] / percentage);
}
}
}
mvs = null;
if (buf != null) {
LOG.debugFine(buf.toString());
}
}
/**
* Algorithm 3 of Cheng and Church.
*
* <p>Try to re-add rows or columns that decrease the overall score.
*
* <p>Also try adding inverted rows.
*
* @param mat Data matrix
* @param cand Bicluster candidate
*/
private void nodeAddition(final double[][] mat, final BiclusterCandidate cand) {
cand.updateRowAndColumnMeans(mat, true);
cand.computeMeanSquaredDeviation(mat);
while (true) {
// We need this to be final + mutable
final boolean[] added = new boolean[] {false, false};
// Step 2: add columns
cand.visitRow(
mat,
0,
CellVisitor.NOT_SELECTED,
new CellVisitor() {
@Override
public boolean visit(double val, int row, int col, boolean selrow, boolean selcol) {
assert (!selcol);
if (cand.computeColResidue(mat, col) <= cand.residue) {
cand.selectColumn(col, true);
added[0] = true;
}
return false;
}
});
// Step 3: recompute values
if (added[0]) {
cand.updateRowAndColumnMeans(mat, true);
cand.computeMeanSquaredDeviation(mat);
}
// Step 4: try adding rows.
cand.visitColumn(
mat,
0,
CellVisitor.NOT_SELECTED,
new CellVisitor() {
@Override
public boolean visit(double val, int row, int col, boolean selrow, boolean selcol) {
assert (!selrow);
if (cand.computeRowResidue(mat, row, false) <= cand.residue) {
cand.selectRow(row, true);
added[1] = true;
}
return false;
}
});
// Step 5: try adding inverted rows.
if (useinverted) {
cand.visitColumn(
mat,
0,
CellVisitor.NOT_SELECTED,
new CellVisitor() {
@Override
public boolean visit(double val, int row, int col, boolean selrow, boolean selcol) {
assert (!selrow);
if (cand.computeRowResidue(mat, row, true) <= cand.residue) {
cand.selectRow(row, true);
cand.invertRow(row, true);
added[1] = true;
}
return false;
}
});
}
if (added[1]) {
cand.updateRowAndColumnMeans(mat, true);
cand.computeMeanSquaredDeviation(mat);
if (LOG.isDebuggingFine()) {
LOG.debugFine(
"Residue in Alg 3: " + cand.residue + " " + cand.rowcard + "x" + cand.colcard);
}
}
if (!added[0] && !added[1]) {
break;
}
}
}
/**
* Algorithm 1 of Cheng and Church:
*
* <p>Remove single rows or columns.
*
* <p>Inverted rows are not supported in this method.
*
* @param mat Data matrix
* @param cand Bicluster candidate
*/
private void singleNodeDeletion(final double[][] mat, final BiclusterCandidate cand) {
// Assume that cand.residue is up to date!
while (cand.residue > delta && (cand.colcard > 2 || cand.rowcard > 2)) {
// Store current maximum. Need final mutable, so use arrays.
final double[] max = {Double.NEGATIVE_INFINITY};
final int[] best = {-1, -1};
// Test rows
if (cand.rowcard > 2) {
cand.visitColumn(
mat,
0,
CellVisitor.SELECTED,
new CellVisitor() {
@Override
public boolean visit(double val, int row, int col, boolean selrow, boolean selcol) {
assert (selrow);
double rowResidue = cand.computeRowResidue(mat, row, false);
if (max[0] < rowResidue) {
max[0] = rowResidue;
best[0] = row;
}
return false;
}
});
}
// Test columns:
if (cand.colcard > 2) {
cand.visitRow(
mat,
0,
CellVisitor.SELECTED,
new CellVisitor() {
@Override
public boolean visit(double val, int row, int col, boolean selrow, boolean selcol) {
assert (selcol);
double colResidue = cand.computeColResidue(mat, col);
if (max[0] < colResidue) {
max[0] = colResidue;
best[1] = col;
}
return false;
}
});
}
if (best[1] >= 0) { // then override bestrow!
cand.selectColumn(best[1], false);
} else {
assert (best[0] >= 0);
cand.selectRow(best[0], false);
}
// TODO: incremental update could be much faster?
cand.updateRowAndColumnMeans(mat, false);
cand.computeMeanSquaredDeviation(mat);
if (LOG.isDebuggingFine()) {
LOG.debugFine(
"Residue in Alg 1: " + cand.residue + " " + cand.rowcard + "x" + cand.colcard);
}
}
}
/**
* Algorithm 2 of Cheng and Church.
*
* <p>Remove all rows and columns that reduce the residue by alpha.
*
* <p>Inverted rows are not supported in this method.
*
* @param mat Data matrix
* @param cand Bicluster candidate
*/
private void multipleNodeDeletion(final double[][] mat, final BiclusterCandidate cand) {
cand.updateRowAndColumnMeans(mat, false);
cand.computeMeanSquaredDeviation(mat);
// Note: assumes that cand.residue = H(I,J)
while (cand.residue > delta) {
final boolean[] modified = {false, false};
// Step 2: remove rows above threshold
if (cand.rowcard > MIN_ROW_REMOVE_THRESHOLD) {
final double alphaResidue = alpha * cand.residue;
cand.visitColumn(
mat,
0,
CellVisitor.SELECTED,
new CellVisitor() {
@Override
public boolean visit(double val, int row, int col, boolean selrow, boolean selcol) {
assert (selrow);
if (cand.computeRowResidue(mat, row, false) > alphaResidue) {
cand.selectRow(row, false);
modified[0] = true;
}
return (cand.rowcard > MIN_ROW_REMOVE_THRESHOLD);
}
});
// Step 3: update residue
if (modified[0]) {
cand.updateRowAndColumnMeans(mat, false);
cand.computeMeanSquaredDeviation(mat);
}
}
// Step 4: remove columns above threshold
if (cand.colcard > MIN_COLUMN_REMOVE_THRESHOLD) {
final double alphaResidue = alpha * cand.residue;
cand.visitRow(
mat,
0,
CellVisitor.SELECTED,
new CellVisitor() {
@Override
public boolean visit(double val, int row, int col, boolean selrow, boolean selcol) {
assert (selcol);
if (cand.computeColResidue(mat, col) > alphaResidue) {
cand.selectColumn(col, false);
modified[1] = true;
}
return (cand.colcard > MIN_COLUMN_REMOVE_THRESHOLD);
}
});
if (modified[1]) {
cand.updateRowAndColumnMeans(mat, false);
cand.computeMeanSquaredDeviation(mat);
}
}
if (LOG.isDebuggingFine()) {
LOG.debugFine(
"Residue in Alg 2: " + cand.residue + " " + cand.rowcard + "x" + cand.colcard);
}
// Step 5: if nothing has been removed, try removing single nodes.
if (!modified[0] && !modified[1]) {
break;
// Will be executed next in main loop, as per algorithm 4.
// singleNodeDeletion();
}
}
}
/**
* Performs the SUBCLU algorithm on the given database.
*
* @param relation Relation to process
* @return Clustering result
*/
public Clustering<SubspaceModel> run(Relation<V> relation) {
final int dimensionality = RelationUtil.dimensionality(relation);
StepProgress stepprog = LOG.isVerbose() ? new StepProgress(dimensionality) : null;
// Generate all 1-dimensional clusters
LOG.beginStep(stepprog, 1, "Generate all 1-dimensional clusters.");
// mapping of dimensionality to set of subspaces
HashMap<Integer, List<Subspace>> subspaceMap = new HashMap<>();
// list of 1-dimensional subspaces containing clusters
List<Subspace> s_1 = new ArrayList<>();
subspaceMap.put(0, s_1);
// mapping of subspaces to list of clusters
TreeMap<Subspace, List<Cluster<Model>>> clusterMap =
new TreeMap<>(new Subspace.DimensionComparator());
for (int d = 0; d < dimensionality; d++) {
Subspace currentSubspace = new Subspace(d);
List<Cluster<Model>> clusters = runDBSCAN(relation, null, currentSubspace);
if (LOG.isDebuggingFiner()) {
StringBuilder msg = new StringBuilder();
msg.append('\n')
.append(clusters.size())
.append(" clusters in subspace ")
.append(currentSubspace.dimensonsToString())
.append(": \n");
for (Cluster<Model> cluster : clusters) {
msg.append(" " + cluster.getIDs() + "\n");
}
LOG.debugFiner(msg.toString());
}
if (!clusters.isEmpty()) {
s_1.add(currentSubspace);
clusterMap.put(currentSubspace, clusters);
}
}
// Generate (d+1)-dimensional clusters from d-dimensional clusters
for (int d = 0; d < dimensionality - 1; d++) {
if (stepprog != null) {
stepprog.beginStep(
d + 2,
"Generate "
+ (d + 2)
+ "-dimensional clusters from "
+ (d + 1)
+ "-dimensional clusters.",
LOG);
}
List<Subspace> subspaces = subspaceMap.get(d);
if (subspaces == null || subspaces.isEmpty()) {
if (stepprog != null) {
for (int dim = d + 1; dim < dimensionality - 1; dim++) {
stepprog.beginStep(
dim + 2,
"Generation of"
+ (dim + 2)
+ "-dimensional clusters not applicable, because no more "
+ (d + 2)
+ "-dimensional subspaces found.",
LOG);
}
}
break;
}
List<Subspace> candidates = generateSubspaceCandidates(subspaces);
List<Subspace> s_d = new ArrayList<>();
for (Subspace candidate : candidates) {
Subspace bestSubspace = bestSubspace(subspaces, candidate, clusterMap);
if (LOG.isDebuggingFine()) {
LOG.debugFine(
"best subspace of "
+ candidate.dimensonsToString()
+ ": "
+ bestSubspace.dimensonsToString());
}
List<Cluster<Model>> bestSubspaceClusters = clusterMap.get(bestSubspace);
List<Cluster<Model>> clusters = new ArrayList<>();
for (Cluster<Model> cluster : bestSubspaceClusters) {
List<Cluster<Model>> candidateClusters = runDBSCAN(relation, cluster.getIDs(), candidate);
if (!candidateClusters.isEmpty()) {
clusters.addAll(candidateClusters);
}
}
if (LOG.isDebuggingFine()) {
StringBuilder msg = new StringBuilder();
msg.append(clusters.size() + " cluster(s) in subspace " + candidate + ": \n");
for (Cluster<Model> c : clusters) {
msg.append(" " + c.getIDs() + "\n");
}
LOG.debugFine(msg.toString());
}
if (!clusters.isEmpty()) {
s_d.add(candidate);
clusterMap.put(candidate, clusters);
}
}
if (!s_d.isEmpty()) {
subspaceMap.put(d + 1, s_d);
}
}
// build result
int numClusters = 1;
result = new Clustering<>("SUBCLU clustering", "subclu-clustering");
for (Subspace subspace : clusterMap.descendingKeySet()) {
List<Cluster<Model>> clusters = clusterMap.get(subspace);
for (Cluster<Model> cluster : clusters) {
Cluster<SubspaceModel> newCluster = new Cluster<>(cluster.getIDs());
newCluster.setModel(new SubspaceModel(subspace, Centroid.make(relation, cluster.getIDs())));
newCluster.setName("cluster_" + numClusters++);
result.addToplevelCluster(newCluster);
}
}
LOG.setCompleted(stepprog);
return result;
}
