How to use
SLINKHDBSCANLinearMemory
in
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

Best Java code snippets using de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.SLINKHDBSCANLinearMemory (Showing top 9 results out of 315)

Common ways to obtain SLINKHDBSCANLinearMemory

private void myMethod () {
 SLINKHDBSCANLinearMemory s =DistanceFunction distanceFunction;new SLINKHDBSCANLinearMemory<>(distanceFunction, minPts)
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}

 @Override
 protected SLINKHDBSCANLinearMemory<O> makeInstance() {
  return new SLINKHDBSCANLinearMemory<>(distanceFunction, minPts);
 }
}

final DistanceQuery<O> distQ = db.getDistanceQuery(relation, getDistanceFunction());
final KNNQuery<O> knnQ = db.getKNNQuery(distQ, minPts);
final WritableDoubleDataStore coredists = computeCoreDists(ids, knnQ, minPts);
 step1(id, pi, lambda);
 step2(id, processedIDs, distQ, coredists, m);
 step3(id, pi, lambda, processedIDs, m);
 step4(id, pi, lambda, processedIDs);

@Override
public TypeInformation[] getInputTypeRestriction() {
 return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
}

final DistanceQuery<O> distQ = db.getDistanceQuery(relation, getDistanceFunction());
final KNNQuery<O> knnQ = db.getKNNQuery(distQ, minPts);
final WritableDoubleDataStore coredists = computeCoreDists(ids, knnQ, minPts);
 step1(id, pi, lambda);
 step2(id, processedIDs, distQ, coredists, m);
 step3(id, pi, lambda, processedIDs, m);
 step4(id, pi, lambda, processedIDs);

@Override
public TypeInformation[] getInputTypeRestriction() {
 return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
}

final DistanceQuery<O> distQ = db.getDistanceQuery(relation, getDistanceFunction());
final KNNQuery<O> knnQ = db.getKNNQuery(distQ, minPts);
final WritableDoubleDataStore coredists = computeCoreDists(ids, knnQ, minPts);
 step1(id, pi, lambda);
 step2(id, processedIDs, distQ, coredists, m);
 step3(id, pi, lambda, processedIDs, m);
 step4(id, pi, lambda, processedIDs);

 @Override
 protected SLINKHDBSCANLinearMemory<O> makeInstance() {
  return new SLINKHDBSCANLinearMemory<>(distanceFunction, minPts);
 }
}

@Override
public TypeInformation[] getInputTypeRestriction() {
 return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
}

 @Override
 protected SLINKHDBSCANLinearMemory<O> makeInstance() {
  return new SLINKHDBSCANLinearMemory<>(distanceFunction, minPts);
 }
}

Javadoc

Linear memory implementation of HDBSCAN clustering based on SLINK.

By not building a distance matrix, we can reduce memory usage to linear memory only; but at the cost of roughly double the runtime (unless using indexes) as we first need to compute all kNN distances (for core sizes), then recompute distances when building the spanning tree.

This version uses the SLINK algorithm to directly produce the pointer representation expected by the extraction methods. The SLINK algorithm is closely related to Prim's minimum spanning tree, but produces the more compact pointer representation instead of an edges list.

This implementation does not include the cluster extraction discussed as Step 4. This functionality should however already be provided by de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction. For this reason, we also do not include self-edges.

Reference:

R. J. G. B. Campello, D. Moulavi, J. Sander
Density-Based Clustering Based on Hierarchical Density Estimates
Pacific-Asia Conf. Advances in Knowledge Discovery and Data Mining (PAKDD)

Most used methods

<init>
Constructor.
computeCoreDists
getDistanceFunction
step1
First step: Initialize P(id) = id, L(id) = infinity.
step2
Second step: Determine the pairwise distances from all objects in the pointer representation to the
step3
Third step: Determine the values for P and L
step4
Fourth step: Actualize the clusters if necessary

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How to useSLINKHDBSCANLinearMemory in de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

Best Java code snippets using de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.SLINKHDBSCANLinearMemory (Showing top 9 results out of 315)

How to use
SLINKHDBSCANLinearMemory
in
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical