1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * http://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17
18 package org.apache.commons.math4.legacy.ml.clustering;
19
20 import java.util.Collection;
21 import java.util.List;
22
23 import org.apache.commons.math4.legacy.ml.clustering.evaluation.SumOfClusterVariances;
24
25 /**
26 * A wrapper around a k-means++ clustering algorithm which performs multiple trials
27 * and returns the best solution.
28 * @param <T> type of the points to cluster
29 * @since 3.2
30 */
31 public class MultiKMeansPlusPlusClusterer<T extends Clusterable> extends Clusterer<T> {
32
33 /** The underlying k-means clusterer. */
34 private final KMeansPlusPlusClusterer<T> clusterer;
35
36 /** The number of trial runs. */
37 private final int numTrials;
38
39 /** The cluster evaluator to use. */
40 private final ClusterRanking evaluator;
41
42 /** Build a clusterer.
43 * @param clusterer the k-means clusterer to use
44 * @param numTrials number of trial runs
45 */
46 public MultiKMeansPlusPlusClusterer(final KMeansPlusPlusClusterer<T> clusterer,
47 final int numTrials) {
48 this(clusterer,
49 numTrials,
50 ClusterEvaluator.ranking(new SumOfClusterVariances(clusterer.getDistanceMeasure())));
51 }
52
53 /** Build a clusterer.
54 * @param clusterer the k-means clusterer to use
55 * @param numTrials number of trial runs
56 * @param evaluator the cluster evaluator to use
57 * @since 3.3
58 */
59 public MultiKMeansPlusPlusClusterer(final KMeansPlusPlusClusterer<T> clusterer,
60 final int numTrials,
61 final ClusterRanking evaluator) {
62 super(clusterer.getDistanceMeasure());
63 this.clusterer = clusterer;
64 this.numTrials = numTrials;
65 this.evaluator = evaluator;
66 }
67
68 /**
69 * Runs the K-means++ clustering algorithm.
70 *
71 * @param points the points to cluster
72 * @return a list of clusters containing the points
73 * @throws org.apache.commons.math4.legacy.exception.MathIllegalArgumentException if
74 * the data points are null or the number of clusters is larger than the
75 * number of data points
76 * @throws org.apache.commons.math4.legacy.exception.ConvergenceException if
77 * an empty cluster is encountered and the underlying {@link KMeansPlusPlusClusterer}
78 * has its {@link KMeansPlusPlusClusterer.EmptyClusterStrategy} is set to {@code ERROR}.
79 */
80 @Override
81 public List<CentroidCluster<T>> cluster(final Collection<T> points) {
82 // at first, we have not found any clusters list yet
83 List<CentroidCluster<T>> best = null;
84 double bestRank = Double.NEGATIVE_INFINITY;
85
86 // do several clustering trials
87 for (int i = 0; i < numTrials; ++i) {
88
89 // compute a clusters list
90 List<CentroidCluster<T>> clusters = clusterer.cluster(points);
91
92 // compute the rank of the current list
93 final double rank = evaluator.compute(clusters);
94
95 if (rank > bestRank) {
96 // this one is the best we have found so far, remember it
97 best = clusters;
98 bestRank = rank;
99 }
100 }
101
102 // return the best clusters list found
103 return best;
104 }
105 }