DSC_DStream | R Documentation |
Micro Clusterer with reclustering. Implements the grid-based D-Stream data stream clustering algorithm.
DSC_DStream( formula = NULL, gridsize, lambda = 0.001, gaptime = 1000L, Cm = 3, Cl = 0.8, attraction = FALSE, epsilon = 0.3, Cm2 = Cm, k = NULL, N = 0 ) get_attraction(x, relative = FALSE, grid_type = "dense", dist = FALSE) ## S3 method for class 'DSC_DStream' plot( x, dsd = NULL, n = 500, type = c("auto", "micro", "macro", "both"), grid = FALSE, grid_type = "used", assignment = FALSE, ... ) DSOutlier_DStream( formula = NULL, gridsize, lambda = 0.001, gaptime = 1000L, Cm = 3, Cl = 0.8, outlier_multiplier = 2 )
formula |
|
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 |
epsilon |
overlap parameter for attraction as a proportion of
|
Cm2 |
threshold on attraction to join two dense grid cells (as a
proportion on the average expected attraction). In the original algorithm
|
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 |
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. |
dsd |
a DSD data stream object. |
n |
number of plots taken from |
type |
Plot micro clusters ( |
grid |
logical; show the D-Stream grid instead of circles for micro-clusters. |
assignment |
logical; show assignment area of micro-clusters. |
... |
further argument are passed on. |
outlier_multiplier |
multiplier for assignment grid width to declare outliers. |
D-Stream 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 D-Stream (2007 version) merges adjacent dense grids to form
macro-clusters and then assigns adjacent transitional grids to
macro-clusters. 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
macro-clusters 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()
), D-Stream
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 D-Stream for reclustering.
For plot()
D-Stream also provides extra parameters "grid"
and
"grid_type"
to show micro-clusters as grid cells (density represented
by gray values).
DSOutlier_DStream
classifies points that do not fall into a dense grid cell as
outlier/noise. Parameter outlier_multiplier
specifies
how far the point needs to be away from a dense cell to be classified as an outlier by multiplying the grid
size.
Note that DSC_DStream
currently cannot be saved to disk using
save() or saveRDS(). This functionality will be added later!
An object of class DSC_DStream
(subclass of DSC,
DSC_R, DSC_Micro).
Michael Hahsler
Yixin Chen and Li Tu. 2007. Density-based clustering for real-time stream data. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '07). ACM, New York, NY, USA, 133-142.
Li Tu and Yixin Chen. 2009. Stream data clustering based on grid density and attraction. ACM Transactions on Knowledge Discovery from Data, 3(3), Article 12 (July 2009), 27 pages.
Other DSC_Micro:
DSC_BICO()
,
DSC_BIRCH()
,
DSC_DBSTREAM()
,
DSC_Micro()
,
DSC_Sample()
,
DSC_Window()
,
DSC_evoStream()
Other DSC_TwoStage:
DSC_DBSTREAM()
,
DSC_TwoStage()
,
DSC_evoStream()
Other DSOutlier:
DSC_DBSTREAM()
,
DSOutlier()
stream <- DSD_BarsAndGaussians(noise = .05) plot(stream) dstream1 <- DSC_DStream(gridsize = 1, Cm = 1.5) update(dstream1, stream, 1000) dstream1 # micro-clusters (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") ### Macro-clusters # standard D-Stream uses reachability nclusters(dstream1, type = "macro") get_centers(dstream1, type = "macro") plot(dstream1, stream, type = "macro") evaluate_static(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, grid = TRUE) evaluate_static(dstream2, stream, measure = "crand", type = "macro")
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