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D-Stream Data Stream Clustering Algorithm

Source: R/DSC_DStream_MOA.R
DSC_DStream_MOA.Rd

This is an interface to the MOA implementation of D-Stream. A C++ implementation (including reclustering with attraction) is available as stream::DSC_DStream.

Usage

DSC_DStream_MOA(decayFactor = 0.998, Cm = 3, Cl = 0.8, Beta = 0.3)

Arguments

decayFactor

Decay factor applied to grid-cell density.

Cm

Threshold for classifying grid cells as dense.

Cl

Threshold for classifying grid cells as sparse.

Beta

Adjusts the window of protection for renaming previously deleted grids as sporadic.

Details

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 decay factor.

Notes

  • The implementation uses a 1-by-1 grid, so the example expands the data range.

  • The MOA implementation does not currently return micro-clusters.

References

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.

See also

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

Author

Matthias Carnein

Examples

set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05, space_limit = c(0, 10))

# cluster with D-Stream
dstream <- DSC_DStream_MOA(Cm = 3)
update(dstream, stream, 1000)
dstream
#> DStream 
#> Class: moa/clusterers/dstream/Dstream, DSC_MOA, DSC_Micro, DSC 
#> Number of macro-clusters: 3 

# plot macro-clusters
plot(dstream, stream, type = "macro")