@Title(value="FDBSCAN: Density-based Clustering of Applications with Noise on fuzzy objects") @Description(value="Algorithm to find density-connected sets in a database consisting of uncertain/fuzzy objects based on the parameters \'minpts\', \'epsilon\', \'samplesize\', and (if used) \'threshold\'") @Reference(authors="H.-P. Kriegel and M. Pfeifle", title="Density-based clustering of uncertain data", booktitle="KDD05", url="http://dx.doi.org/10.1145/1081870.1081955") public class FDBSCAN extends GeneralizedDBSCAN
FDBSCANNeighborPredicate
.
Reference:
H.-P. Kriegel and M. Pfeifle:
Density-based clustering of uncertain data
In Proc. 11th ACM Int. Conf. on Knowledge Discovery and Data Mining (SIGKDD),
Chicago, IL, 2005.
Modifier and Type | Class and Description |
---|---|
static class |
FDBSCAN.Parameterizer
Parameterizer class.
|
GeneralizedDBSCAN.Instance<T>
coremodel, corepred, npred
Constructor and Description |
---|
FDBSCAN(double epsilon,
int sampleSize,
double threshold,
RandomFactory seed,
int minpts)
Constructor that initialized GeneralizedDBSCAN.
|
getInputTypeRestriction, getLogger, run
makeParameterDistanceFunction
public FDBSCAN(double epsilon, int sampleSize, double threshold, RandomFactory seed, int minpts)
epsilon
- Epsilon radiussampleSize
- Sample sizethreshold
- Thresholdseed
- Random generatorminpts
- MinPtsCopyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.