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de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical

Best Java code snippets using de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.CLINK (Showing top 6 results out of 315)

origin: elki-project/elki

 @Override
 protected CLINK<O> makeInstance() {
  return new CLINK<>(distanceFunction);
 }
}
origin: elki-project/elki

/**
 * CLINK main loop, based on the SLINK main loop.
 *
 * @param id Current object
 * @param ids All objects
 * @param it Array iterator
 * @param n Last object to process at this run
 * @param pi Parent
 * @param lambda Height
 * @param m Distance
 */
@Override
protected void process(DBIDRef id, ArrayDBIDs ids, DBIDArrayIter it, int n, WritableDBIDDataStore pi, WritableDoubleDataStore lambda, WritableDoubleDataStore m) {
 clinkstep3(id, it, n, pi, lambda, m);
 clinkstep4567(id, ids, it, n, pi, lambda, m);
 clinkstep8(id, it, n, pi, lambda, m);
}
origin: de.lmu.ifi.dbs.elki/elki

/**
 * CLINK main loop, based on the SLINK main loop.
 *
 * @param id Current object
 * @param ids All objects
 * @param it Array iterator
 * @param n Last object to process at this run
 * @param pi Parent
 * @param lambda Height
 * @param m Distance
 */
@Override
protected void process(DBIDRef id, ArrayDBIDs ids, DBIDArrayIter it, int n, WritableDBIDDataStore pi, WritableDoubleDataStore lambda, WritableDoubleDataStore m) {
 clinkstep3(id, it, n, pi, lambda, m);
 clinkstep4567(id, ids, it, n, pi, lambda, m);
 clinkstep8(id, it, n, pi, lambda, m);
}
origin: de.lmu.ifi.dbs.elki/elki-clustering

/**
 * CLINK main loop, based on the SLINK main loop.
 *
 * @param id Current object
 * @param ids All objects
 * @param it Array iterator
 * @param n Last object to process at this run
 * @param pi Parent
 * @param lambda Height
 * @param m Distance
 */
@Override
protected void process(DBIDRef id, ArrayDBIDs ids, DBIDArrayIter it, int n, WritableDBIDDataStore pi, WritableDoubleDataStore lambda, WritableDoubleDataStore m) {
 clinkstep3(id, it, n, pi, lambda, m);
 clinkstep4567(id, ids, it, n, pi, lambda, m);
 clinkstep8(id, it, n, pi, lambda, m);
}
origin: de.lmu.ifi.dbs.elki/elki

 @Override
 protected CLINK<O> makeInstance() {
  return new CLINK<>(distanceFunction);
 }
}
origin: de.lmu.ifi.dbs.elki/elki-clustering

 @Override
 protected CLINK<O> makeInstance() {
  return new CLINK<>(distanceFunction);
 }
}
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchicalCLINK

Javadoc

CLINK algorithm for complete linkage.

This algorithm runs in O(n²) time, and needs only O(n) memory. The results can differ from the standard algorithm in unfavorable ways, and are order-dependent (Defays: "Modifications of the labeling permit us to obtain different minimal superior ultrametric dissimilarities"). Unfortunately, the results are usually perceived to be substantially worse than the more expensive algorithms for complete linkage clustering. This arises from the fact that this algorithm has to add the new object to the existing tree in every step, instead of being able to always do the globally best merge.

Reference:

D. Defays
An Efficient Algorithm for the Complete Link Cluster Method
In: The Computer Journal 20.4

Most used methods

  • <init>
    Constructor.
  • clinkstep3
    Third step: Determine the values for P and L
  • clinkstep4567
    Fourth to seventh step of CLINK: find best insertion
  • clinkstep8
    Update hierarchy.

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