DSC_DStream
DStream Data Stream Clustering Algorithm
Implements the DStream data stream clustering algorithm.
Usage
DSC_DStream(gridsize, lambda = 1e3, gaptime=1000L, Cm=3, Cl=.8, attraction=FALSE, epsilon=.3, Cm2=Cm, k=NULL, N = 0)
get_attraction(x, relative=FALSE, grid_type = "dense", dist=FALSE)
Arguments
 gridsize
 Size of grid cells.
 lambda
 Fading constant used function to calculate the decay factor $2^lambda$. (Note: in the paper the authors use lamba to denote the decay factor and not the fading constant!)
 gaptime
 sporadic grids are removed every gaptime number of points.
 Cm
 density threshold used to detect dense grids as a proportion of the average expected density (Cm > 1). The average density is given by the total weight of the clustering over $N$, the number of grid cells.
 Cl
 density threshold to detect sporadic grids (0 > Cl > Cm). Transitional grids have a density between Cl and Cm.
 attraction
 compute and store information about the
attraction between
adjacent grids. If
TRUE
then attraction is used to create macroclusters, otherwise macroclusters are created by merging adjacent dense grids.  epsilon
 overlap parameter for attraction as a proportion of
gridsize
.  Cm2
 threshold on attraction to join two dense grid cells
(as a proportion on the average expected attraction).
In the original algorithm
Cm2
is equal toCm
.  k
 alternative to Cm2 (not in the original algorithm). Create k clusters based on attraction. In case of more than k unconnected components, closer groups of MCs are joined.
 N
 Fix the number of grid cells used for the calculation
of the density thresholds with Cl and Cm. If
N
is not given (0) then the algorithm tries to determine N from the data. Note that this means that N potentially increases over time and outliers might produce an extremely large value which will lead to a sudden creation of too many dense microclusters. The original paper assumed that N is known a priori.  x
 DSC_DStream object to get attraction values from.
 relative
 calculates relative attraction (normalized by the cluster weight).
 grid_type
 the attraction between what grid types should be returned?
 dist
 make attraction symmetric and transform into a distance.
Details
DStream creates an equally spaced grid and estimates the density in each
grid cell using the count of points falling in the cells. Grid cells are
classified based on density into dense, transitional and sporadic cells.
The density is faded after every new point by a factor of $2^{lambda}$.
Every gaptime number of points sporadic grid cells are removed.
For reclustering DStream (2007 version) merges adjacent dense grids to
form macroclusters and then assigns adjacent transitional grids to
macroclusters. This behavior is implemented as attraction=FALSE
.
The 2009 version of the algorithm adds the concept of attraction between
grids cells. If attraction=TRUE
is used then the algorithm
produces macroclusters based on attraction between dense adjacent grids
(uses Cm2
which in the original algorithm is equal to Cm
).
For many functions (e.g., get_centers()
, plot()
),
DStream adds a parameter grid_type
with possible
values of "dense"
, "transitional"
, "sparse"
,
"all"
and "used"
. This only returns the selected type
of grid cells. "used"
includes dense and adjacent transitional cells
which are used in DStream for reclustering.
For plot DStream also provides extra parameters "grid"
and
"grid_type"
to show microclusters as grid cells
(density represented by gray values).
Note that DSC_DStream
can at this point not be saved to disk using
save() or saveRDS(). This functionality will be added later!
Value

An object of class
DSC_DStream
(subclass of DSC
, DSC_R
, DSC_Micro
).
References
Yixin Chen and Li Tu. 2007. Densitybased clustering for realtime stream data. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '07). ACM, New York, NY, USA, 133142.
Li Tu and Yixin Chen. 2009. Stream data clustering based on grid density and attraction. ACM Trans. Knowl. Discov. Data 3, 3, Article 12 (July 2009), 27 pages.
See Also
Examples
stream < DSD_BarsAndGaussians(noise=.05)
plot(stream)
# we set Cm=.8 to pick up the lower density clusters
dstream1 < DSC_DStream(gridsize=1, Cm=1.5)
update(dstream1, stream, 1000)
dstream1
# microclusters (these are "used" grid cells)
nclusters(dstream1)
head(get_centers(dstream1))
# plot (DStream provides additional grid visualization)
plot(dstream1, stream)
plot(dstream1, stream, grid=TRUE)
# look only at dense grids
nclusters(dstream1, grid_type="dense")
plot(dstream1, stream, grid=TRUE, grid_type="dense")
# look at transitional and sparse cells
plot(dstream1, stream, grid=TRUE, grid_type="transitional")
plot(dstream1, stream, grid=TRUE, grid_type="sparse")
### Macroclusters
# standard DStream uses reachability
nclusters(dstream1, type="macro")
get_centers(dstream1, type="macro")
plot(dstream1, stream, type="both", grid=TRUE)
evaluate(dstream1, stream, measure="crand", type="macro")
# use attraction for reclustering
dstream2 < DSC_DStream(gridsize=1, attraction=TRUE, Cm=1.5)
update(dstream2, stream, 1000)
dstream2
plot(dstream2, stream, type="both", grid=TRUE)
evaluate(dstream2, stream, measure="crand", type="macro")