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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.
 *******************************************************************************/

/**
 * 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;