de.lmu.ifi.dbs.elki.algorithm.clustering
Class DBSCAN<O extends DatabaseObject,D extends Distance<D>>

java.lang.Object
  de.lmu.ifi.dbs.elki.logging.AbstractLoggable
      de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<O,R>
          de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm<O,D,Clustering<Model>>
              de.lmu.ifi.dbs.elki.algorithm.clustering.DBSCAN<O,D>

Type Parameters:

O - the type of DatabaseObject the algorithm is applied on

D - the type of Distance used

All Implemented Interfaces:

Algorithm<O,Clustering<Model>>, ClusteringAlgorithm<Clustering<Model>,O>, Parameterizable

@Title(value="DBSCAN: Density-Based Clustering of Applications with Noise")
@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. Ester, H.-P. Kriegel, J. Sander, and X. Xu",
           title="A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise",
           booktitle="Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD \'96), Portland, OR, 1996",
           url="http://dx.doi.org/10.1145/93605.98741")
public class DBSCAN<O extends DatabaseObject,D extends Distance<D>>
extends DistanceBasedAlgorithm<O,D,Clustering<Model>>
implements ClusteringAlgorithm<Clustering<Model>,O>

DBSCAN provides the DBSCAN algorithm, an algorithm to find density-connected sets in a database.

Reference:
M. Ester, H.-P. Kriegel, J. Sander, and X. Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.
In Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD '96), Portland, OR, 1996.

Author:

Arthur Zimek

Field Summary

private D	epsilon Holds the value of EPSILON_PARAM.
static OptionID	EPSILON_ID OptionID for EPSILON_PARAM
private DistanceParameter<D>	EPSILON_PARAM Parameter to specify the maximum radius of the neighborhood to be considered, must be suitable to the distance function specified.
protected int	minpts Holds the value of MINPTS_PARAM.
static OptionID	MINPTS_ID OptionID for MINPTS_PARAM
private IntParameter	MINPTS_PARAM Parameter to specify the threshold for minimum number of points in the epsilon-neighborhood of a point, must be an integer greater than 0.
protected Set<Integer>	noise Holds a set of noise.
protected Set<Integer>	processedIDs Holds a set of processed ids.
protected List<List<Integer>>	resultList Holds a list of clusters found.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID, DISTANCE_FUNCTION_PARAM

Fields inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debug, logger

Constructor Summary

DBSCAN(Parameterization config)
Constructor, adhering to Parameterizable

Method Summary

protected void	expandCluster(Database<O> database, Integer startObjectID, FiniteProgress objprog, IndefiniteProgress clusprog) DBSCAN-function expandCluster.
protected Clustering<Model>	runInTime(Database<O> database) Performs the DBSCAN algorithm on the given database.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

getDistanceFactory, getDistanceFunction

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

isTime, isVerbose, run, setTime, setVerbose

Methods inherited from class de.lmu.ifi.dbs.elki.logging.AbstractLoggable

debugFine, debugFiner, debugFinest, exception, progress, verbose, warning

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm

run

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.Algorithm

setTime, setVerbose

Field Detail

EPSILON_ID

public static final OptionID EPSILON_ID

OptionID for EPSILON_PARAM

EPSILON_PARAM

private final DistanceParameter<D extends Distance<D>> EPSILON_PARAM

Parameter to specify the maximum radius of the neighborhood to be considered, must be suitable to the distance function specified.

Key: -dbscan.epsilon

epsilon

private D extends Distance<D> epsilon

Holds the value of EPSILON_PARAM.

MINPTS_ID

public static final OptionID MINPTS_ID

OptionID for MINPTS_PARAM

MINPTS_PARAM

private final IntParameter MINPTS_PARAM

Parameter to specify the threshold for minimum number of points in the epsilon-neighborhood of a point, must be an integer greater than 0.

Key: -dbscan.minpts

minpts

protected int minpts

Holds the value of MINPTS_PARAM.

resultList

protected List<List<Integer>> resultList

Holds a list of clusters found.

noise

protected Set<Integer> noise

Holds a set of noise.

processedIDs

protected Set<Integer> processedIDs

Holds a set of processed ids.

Constructor Detail

DBSCAN

public DBSCAN(Parameterization config)

Constructor, adhering to Parameterizable

Parameters:

config - Parameterization

Method Detail

runInTime

protected Clustering<Model> runInTime(Database<O> database)
                               throws IllegalStateException

Performs the DBSCAN algorithm on the given database.

Specified by:

runInTime in class AbstractAlgorithm<O extends DatabaseObject,Clustering<Model>>

Parameters:

database - the database to run the algorithm on

Returns:

the Result computed by this algorithm

Throws:

IllegalStateException - if the algorithm has not been initialized properly (e.g. the setParameters(String[]) method has been failed to be called).

expandCluster

protected void expandCluster(Database<O> database,
                             Integer startObjectID,
                             FiniteProgress objprog,
                             IndefiniteProgress clusprog)

DBSCAN-function expandCluster.

Border-Objects become members of the first possible cluster.

Parameters:

database - the database on which the algorithm is run

startObjectID - potential seed of a new potential cluster

objprog - the progress object for logging the current status