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Micro-cluster Continuous Outlier Detector (MCOD)

Source: R/DSOutlier_MCOD.R
DSC_MCOD.Rd

Interface to 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

Radius used to search for neighbors.

t

Minimum number of neighbors required for a point not to be an outlier.

w

Sliding window width in data points.

recheck_outliers

If TRUE, allow detected outliers to be checked again.

x

A DSC_MCOD object.

...

Further arguments (currently ignored).

outlier_correlated_id

Identifier of the outlier to check again.

Value

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

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 with get_outlier_positions() and checked again with recheck_outlier().

Note: The implementation updates the clustering when predict() is called.

Functions

  • get_outlier_positions(): Returns spatial positions of all current outliers.

  • recheck_outlier(): Re-check whether the outlier identified by outlier_correlated_id is still an outlier. Returns TRUE if it is.

  • clean_outliers(): Forget detected outliers (currently not implemented).

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()

Author

Dalibor Krleža

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
#> Micro-cluster outlier detector 
#> Class: DSC_MCOD, DSC_Micro, DSC_MOA, DSC 
#> Number of micro-clusters: 7 

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)


# Retrieve detected outlier positions.
get_outlier_positions(mcod)
#>         X1         X2
#> 1 0.491609 0.04416643

# Example 3: evaluate on a stream
evaluate_static(mcod, stream, n = 100, type = "micro",
  measure = c("crand", "noisePrecision", "outlierjaccard"))
#> Evaluation results for micro-clusters.
#> Points were assigned to micro-clusters.
#> 
#>          cRand noisePrecision outlierJaccard 
#>      0.5138289      1.0000000      1.0000000 
#> attr(,"type")
#> [1] "micro"
#> attr(,"assign")
#> [1] "micro"