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/*
 * Copyright (c) 2010-2021 Haifeng Li. All rights reserved.
 *
 * Smile is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Smile is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Smile.  If not, see .
 */

/**
 * Cluster dissimilarity measures. An agglomerative hierarchical clustering
 * builds the hierarchy from the individual elements by progressively merging
 * clusters. The linkage criteria determines the distance between clusters
 * (i.e. sets of observations) based on as a pairwise distance function between
 * observations. Some commonly used linkage criteria are
 * 
 *  Maximum or complete linkage clustering 
 *  Minimum or single-linkage clustering 
 *  Mean or average linkage clustering, or UPGMA 
 *  Unweighted Pair Group Method using Centroids, or UPCMA (also known as centroid linkage) 
 *  Weighted Pair Group Method with Arithmetic mean, or WPGMA. 
 *  Weighted Pair Group Method using Centroids, or WPGMC (also known as median linkage) 
 *  Ward's linkage
 * 
 * 
 * @author Haifeng Li
 */
package smile.clustering.linkage;