DSC_MCOD {streamMOA}

Micro-cluster Continuous Outlier Detector (MCOD)

Description

Class interfaces the MOA implementation of the MCOD algorithm for distance-based data stream outlier detection.

Usage

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, ...)

Arguments

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 DSC_MCOD object.

...

further arguments are currently ignored.

outlier_correlated_id

ids of outliers.

Details

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.

Value

An object of class DSC_MCOD (subclass of stream::DSC_Micro, DSC_MOA and stream::DSC).

Functions

• 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).

Author(s)

Dalibor Krleža

References

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

See Also

Other DSC_MOA: DSC_BICO_MOA(), DSC_CluStream(), DSC_ClusTree(), DSC_DStream_MOA(), DSC_DenStream(), DSC_MOA(), DSC_StreamKM()

Examples

# 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"))