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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;

/**
 * A measure of dissimilarity between clusters (i.e. sets of observations).
 * 
 * 

References

*
    *
  1. Anil K. Jain, Richard C. Dubes. Algorithms for clustering data. 1988.
  2. *
* * @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); }




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