
O - the type of DatabaseObjects handled by the algorithm@Title(value="OPTICS: Density-Based Hierarchical Clustering") @Description(value="Algorithm to find density-connected sets in a database based on the parameters \'minPts\' and \'epsilon\' (specifying a volume). These two parameters determine a density threshold for clustering.") @Reference(authors="M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander", title="OPTICS: Ordering Points to Identify the Clustering Structure", booktitle="Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD \'99)", url="http://dx.doi.org/10.1145/304181.304187") @Alias(value={"OPTICS","de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICS"}) public abstract class AbstractOPTICS<O> extends AbstractDistanceBasedAlgorithm<O,ClusterOrder> implements OPTICSTypeAlgorithm
M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander:
OPTICS: Ordering Points to Identify the Clustering Structure.
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '99).
| Modifier and Type | Class and Description |
|---|---|
static class |
AbstractOPTICS.Parameterizer<O>
Parameterization class.
|
| Modifier and Type | Field and Description |
|---|---|
protected double |
epsilon
Holds the maximum distance to search for objects (performance parameter)
|
protected int |
minpts
The density threshold, in number of points.
|
DISTANCE_FUNCTION_ID| Constructor and Description |
|---|
AbstractOPTICS(DistanceFunction<? super O> distanceFunction,
double epsilon,
int minpts)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
TypeInformation[] |
getInputTypeRestriction()
Get the input type restriction used for negotiating the data query.
|
int |
getMinPts()
Get the minpts value used.
|
abstract ClusterOrder |
run(Database db,
Relation<O> relation)
Run OPTICS on the database.
|
getDistanceFunctiongetLogger, makeParameterDistanceFunction, runclone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitrunprotected double epsilon
protected int minpts
public AbstractOPTICS(DistanceFunction<? super O> distanceFunction, double epsilon, int minpts)
distanceFunction - Distance functionepsilon - Epsilon valueminpts - Minpts valuepublic abstract ClusterOrder run(Database db, Relation<O> relation)
db - Databaserelation - Relationpublic int getMinPts()
OPTICSTypeAlgorithmgetMinPts in interface OPTICSTypeAlgorithmpublic TypeInformation[] getInputTypeRestriction()
AbstractAlgorithmgetInputTypeRestriction in interface AlgorithmgetInputTypeRestriction in class AbstractAlgorithm<ClusterOrder>Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.