smile.clustering.linkage.SingleLinkage Maven / Gradle / Ivy
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/*******************************************************************************
* Copyright (c) 2010 Haifeng Li
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
package smile.clustering.linkage;
/**
* Single linkage. The distance between groups is defined as the distance
* between the closest pair of objects, one from each group.
* A drawback of this method is the so-called chaining phenomenon: clusters
* may be forced together due to single elements being close to each other,
* even though many of the elements in each cluster may be very distant to
* each other.
*
* Single linkage clustering is essentially the same as Kruskal's algorithm
* for minimum spanning trees. However, in single linkage clustering, the
* order in which clusters are formed is important, while for minimum spanning
* trees what matters is the set of pairs of points that form distances chosen
* by the algorithm.
*
* @author Haifeng Li
*/
public class SingleLinkage extends Linkage {
/**
* Constructor.
* @param proximity The proximity matrix to store the distance measure of
* dissimilarity. To save space, we only need the lower half of matrix.
*/
public SingleLinkage(double[][] proximity) {
init(proximity);
}
@Override
public String toString() {
return "single linkage";
}
@Override
public void merge(int i, int j) {
for (int k = 0; k < i; k++) {
proximity[index(i, k)] = Math.min(d(i, k), d(j, k));
}
for (int k = i+1; k < size; k++) {
proximity[index(k, i)] = Math.min(d(k, i), d(j, k));
}
}
}