/*******************************************************************************
* 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;
/**
* A measure of dissimilarity between clusters (i.e. sets of observations).
*
* <h2>References</h2>
* <ol>
* <li> Anil K. Jain, Richard C. Dubes. Algorithms for clustering data. 1988.</li>
* </ol>
*
* @see smile.clustering.HierarchicalClustering
*
* @author Haifeng Li
*/
public abstract class Linkage {
/**
* The proximity matrix to store the pair-wise distance measure as
* dissimilarity between clusters. To save space, we only need the
* lower half of matrix. During the clustering, this matrix will be
* updated to reflect the dissimilarity of merged clusters.
*/
double[][] proximity;
/**
* Returns the proximity matrix.
*/
public double[][] getProximity() {
return proximity;
}
/**
* Returns the distance/dissimilarity between two clusters/objects, which
* are indexed by integers.
*/
double d(int i, int j) {
if (i > j)
return proximity[i][j];
else
return proximity[j][i];
}
/**
* Merge two clusters into one and update the proximity matrix.
* @param i cluster id.
* @param j cluster id.
*/
public abstract void merge(int i, int j);
}