DSC_DBSCAN
DBSCAN Macroclusterer
Implements the DBSCAN algorithm for reclustering microclusterings.
Usage
DSC_DBSCAN(eps, MinPts = 5, weighted = TRUE, description=NULL)
Arguments
 eps
 radius of the epsneighborhood.
 MinPts
 minimum number of points required in the epsneighborhood.
 weighted
 logical indicating if a weighted version of DBSCAN should be used.
 description
 optional character string to describe the clustering method.
Details
DBSCAN is a weighted extended version of the implementation in fpc where each microcluster center considered a pseudo point. For weighting we use in the MinPts comparison the sum of weights of the microcluster instead of the number.
DBSCAN first finds core points based on the number of other points in its epsneighborhood. Then core points are joined into clusters using reachability (overlapping epsneighborhoods).
Note that this clustering cannot be updated iteratively and every time it is used for (re)clustering, the old clustering is deleted.
Value

An object of class
DSC_DBSCAN
(a subclass of DSC
,
DSC_R
, DSC_Macro
).
References
Martin Ester, HansPeter Kriegel, Joerg Sander, Xiaowei Xu (1996). A densitybased algorithm for discovering clusters in large spatial databases with noise. In Evangelos Simoudis, Jiawei Han, Usama M. Fayyad. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD96). AAAI Press. pp. 226231.
See Also
Examples
# 3 clusters with 5% noise
stream < DSD_Gaussians(k=3, d=2, noise=0.05)
# Use DBSCAN to recluster micro clusters (a sample)
sample < DSC_Sample(k=100)
update(sample, stream, 500)
dbscan < DSC_DBSCAN(eps = .05)
recluster(dbscan, sample)
plot(dbscan, stream, type="both")
# For comparison we can cluster some data with DBSCAN directly
# Note: DBSCAN is not suitable for data streams since it passes over the data
# several times.
dbscan < DSC_DBSCAN(eps = .05)
update(dbscan, stream, 500)
plot(dbscan, stream)