View source: R/DSOutlier_MCOD.R
DSC_MCOD | R Documentation |
Class interfaces the MOA implementation of the MCOD algorithm for distance-based data stream outlier detection.
DSC_MCOD(r = 0.1, t = 50, w = 1000, recheck_outliers = FALSE) DSOutlier_MCOD(r = 0.1, t = 50, w = 1000, recheck_outliers = TRUE) get_outlier_positions(x, ...) recheck_outlier(x, outlier_correlated_id, ...) clean_outliers(x, ...)
r |
Defines the micro-cluster radius. |
t |
Defines the number of neighbors (k in the article). |
w |
Defines the window width in data points. |
recheck_outliers |
Defines that the MCOD algorithm allows re-checking of detected outliers. |
x |
a |
... |
further arguments are currently ignored. |
outlier_correlated_id |
ids of outliers. |
The algorithm detects density-based outliers. An object x is defined to be an outlier if there are less than t objects lying at distance at most r from x.
Outliers are stored and can be retrieved using get_outlier_position()
and
recheck_outlier()
.
Note: The implementation updates the clustering when predict()
is called.
An object of class DSC_MCOD
(subclass of DSOutlier,
DSC_Micro, DSC_MOA and DSC).
get_outlier_positions()
: Returns spatial positions of all current outliers.
recheck_outlier()
: DSC_MCOD Re-checks the outlier having outlier_correlated_id
.
If this object is still an outlier, the method returns TRUE
.
clean_outliers()
: forget detected outliers from the outlier detector (currently not implemented).
Dalibor Krleža
Kontaki M, Gounaris A, Papadopoulos AN, Tsichlas K, and Manolopoulos Y (2016). Efficient and flexible algorithms for monitoring distance-based outliers over data streams. Information Systems, Vol. 55, pp. 37-53. doi: 10.1109/ICDE.2011.5767923
Other DSC_MOA:
DSC_BICO_MOA()
,
DSC_CluStream()
,
DSC_ClusTree()
,
DSC_DStream_MOA()
,
DSC_DenStream()
,
DSC_MOA()
,
DSC_StreamKM()
# Example 1: Clustering with MCOD stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05) mcod <- DSC_MCOD(r = .1, t = 3, w = 100) update(mcod, stream, 100) mcod plot(mcod, stream, n = 100) # Example 2: Predict outliers (have a class label of NA) stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05) mcod <- DSOutlier_MCOD(r = .1, t = 3, w = 100) update(mcod, stream, 100) plot(mcod, stream, n = 100) # MCOD can retried the outliers get_outlier_positions(mcod) # Example 3: evaluate on a stream evaluate_static(mcod, stream, n = 100, type = "micro", measure = c("crand", "noisePrecision", "outlierjaccard"))
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