1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
|
package de.lmu.ifi.dbs.elki.algorithm.clustering.subspace;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2012
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import de.lmu.ifi.dbs.elki.algorithm.clustering.AbstractProjectedDBSCAN;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.distance.distancefunction.LocallyWeightedDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
import de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj.PreDeConSubspaceIndex;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
/**
* <p/>
* PreDeCon computes clusters of subspace preference weighted connected points.
* The algorithm searches for local subgroups of a set of feature vectors having
* a low variance along one or more (but not all) attributes.
* </p>
* <p/>
* Reference: <br>
* C. Böhm, K. Kailing, H.-P. Kriegel, P. Kröger: Density Connected Clustering
* with Local Subspace Preferences. <br>
* In Proc. 4th IEEE Int. Conf. on Data Mining (ICDM'04), Brighton, UK, 2004.
* </p>
*
* @author Peer Kröger
*
* @apiviz.uses PreDeConSubspaceIndex
*
* @param <V> the type of NumberVector handled by this Algorithm
*/
@Title("PreDeCon: Subspace Preference weighted Density Connected Clustering")
@Description("PreDeCon computes clusters of subspace preference weighted connected points. " + "The algorithm searches for local subgroups of a set of feature vectors having " + "a low variance along one or more (but not all) attributes.")
@Reference(authors = "C. Böhm, K. Kailing, H.-P. Kriegel, P. Kröger", title = "Density Connected Clustering with Local Subspace Preferences", booktitle = "Proc. 4th IEEE Int. Conf. on Data Mining (ICDM'04), Brighton, UK, 2004", url = "http://dx.doi.org/10.1109/ICDM.2004.10087")
public class PreDeCon<V extends NumberVector<V, ?>> extends AbstractProjectedDBSCAN<Clustering<Model>, V> {
/**
* The logger for this class.
*/
private static final Logging logger = Logging.getLogger(PreDeCon.class);
/**
* Constructor.
*
* @param epsilon Epsilon value
* @param minpts MinPts value
* @param distanceFunction outer distance function
* @param lambda Lambda value
*/
public PreDeCon(DoubleDistance epsilon, int minpts, LocallyWeightedDistanceFunction<V> distanceFunction, int lambda) {
super(epsilon, minpts, distanceFunction, lambda);
}
@Override
public String getLongResultName() {
return "PreDeCon Clustering";
}
@Override
public String getShortResultName() {
return "predecon-clustering";
}
@Override
protected Logging getLogger() {
return logger;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<V extends NumberVector<V, ?>> extends AbstractProjectedDBSCAN.Parameterizer<V, DoubleDistance> {
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
configInnerDistance(config);
configEpsilon(config, innerdist);
configMinPts(config);
configOuterDistance(config, epsilon, minpts, PreDeConSubspaceIndex.Factory.class, innerdist);
configLambda(config);
}
@Override
protected PreDeCon<V> makeInstance() {
return new PreDeCon<V>(epsilon, minpts, outerdist, lambda);
}
}
}
|
