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
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
|
package de.lmu.ifi.dbs.elki.algorithm.outlier;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2011
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.database.Database;
import de.lmu.ifi.dbs.elki.database.datastore.DataStore;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.query.DatabaseQuery;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery;
import de.lmu.ifi.dbs.elki.database.query.knn.KNNResult;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
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.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
/**
* Simple distanced based outlier detection algorithm. User has to specify two
* parameters An object is flagged as an outlier if at least a fraction p of all
* data objects has a distance above d from c
* <p>
* Reference: E.M. Knorr, R. T. Ng: Algorithms for Mining Distance-Based
* Outliers in Large Datasets, In: Procs Int. Conf. on Very Large Databases
* (VLDB'98), New York, USA, 1998.
*
* This paper presents several Distance Based Outlier Detection algorithms.
* Implemented here is a simple index based algorithm as presented in section
* 3.1.
*
* @author Lisa Reichert
*
* @apiviz.has KNNQuery
*
* @param <O> the type of DatabaseObjects handled by this Algorithm
* @param <D> the type of Distance used by this Algorithm
*/
@Title("DBOD: Distance Based Outlier Detection")
@Description("If the D-neighborhood of an object contains only very few objects (less than (1-p) percent of the data) this object is flagged as an outlier")
@Reference(authors = "E.M. Knorr, R. T. Ng", title = "Algorithms for Mining Distance-Based Outliers in Large Datasets", booktitle = "Procs Int. Conf. on Very Large Databases (VLDB'98), New York, USA, 1998")
public class DBOutlierDetection<O, D extends Distance<D>> extends AbstractDBOutlier<O, D> {
/**
* The logger for this class.
*/
private static final Logging logger = Logging.getLogger(DBOutlierDetection.class);
/**
* Parameter to specify the minimum fraction of objects that must be outside
* the D- neighborhood of an outlier
*/
public static final OptionID P_ID = OptionID.getOrCreateOptionID("dbod.p", "minimum fraction of objects that must be outside the D-neighborhood of an outlier");
/**
* Holds the value of {@link #P_ID}.
*/
private double p;
/**
* Constructor with actual parameters.
*
* @param distanceFunction distance function parameter
* @param d distance query radius
* @param p percentage parameter
*/
public DBOutlierDetection(DistanceFunction<O, D> distanceFunction, D d, double p) {
super(distanceFunction, d);
this.p = p;
}
@Override
protected DataStore<Double> computeOutlierScores(Database database, Relation<O> relation, D neighborhoodSize) {
DistanceQuery<O, D> distFunc = database.getDistanceQuery(relation, getDistanceFunction());
KNNQuery<O, D> knnQuery = database.getKNNQuery(distFunc, DatabaseQuery.HINT_OPTIMIZED_ONLY);
// maximum number of objects in the D-neighborhood of an outlier
int m = (int) ((distFunc.getRelation().size()) * (1 - p));
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(distFunc.getRelation().getDBIDs(), DataStoreFactory.HINT_STATIC);
if(logger.isVerbose()) {
logger.verbose("computing outlier flag");
}
FiniteProgress progressOFlags = logger.isVerbose() ? new FiniteProgress("DBOutlier for objects", distFunc.getRelation().size(), logger) : null;
int counter = 0;
// if index exists, kNN query. if the distance to the mth nearest neighbor
// is more than d -> object is outlier
if(knnQuery != null) {
for(DBIDIter iditer = distFunc.getRelation().iterDBIDs(); iditer.valid(); iditer.advance()) {
counter++;
final KNNResult<D> knns = knnQuery.getKNNForDBID(iditer, m);
if(logger.isDebugging()) {
logger.debugFine("distance to mth nearest neighbour" + knns.toString());
}
if(knns.get(Math.min(m, knns.size()) - 1).getDistance().compareTo(neighborhoodSize) <= 0) {
// flag as outlier
scores.putDouble(iditer, 1.0);
}
else {
// flag as no outlier
scores.putDouble(iditer, 0.0);
}
}
if(progressOFlags != null) {
progressOFlags.setProcessed(counter, logger);
}
}
else {
// range query for each object. stop if m objects are found
for(DBIDIter iditer = distFunc.getRelation().iterDBIDs(); iditer.valid(); iditer.advance()) {
counter++;
int count = 0;
for (DBIDIter iterator = distFunc.getRelation().iterDBIDs(); iterator.valid() && count < m; iterator.advance()) {
D currentDistance = distFunc.distance(iditer, iterator);
if(currentDistance.compareTo(neighborhoodSize) <= 0) {
count++;
}
}
scores.putDouble(iditer, (count < m) ? 1.0 : 0);
}
if(progressOFlags != null) {
progressOFlags.setProcessed(counter, logger);
}
}
if(progressOFlags != null) {
progressOFlags.ensureCompleted(logger);
}
return scores;
}
@Override
protected Logging getLogger() {
return logger;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<O, D extends Distance<D>> extends AbstractDBOutlier.Parameterizer<O, D> {
protected double p = 0.0;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
final DoubleParameter pP = new DoubleParameter(P_ID);
if(config.grab(pP)) {
p = pP.getValue();
}
}
@Override
protected DBOutlierDetection<O, D> makeInstance() {
return new DBOutlierDetection<O, D>(distanceFunction, d, p);
}
}
}
|
