DSC_DStream_MOA: D-Stream Data Stream Clustering Algorithm

In streamMOA: Interface for MOA Stream Clustering Algorithms

D-Stream Data Stream Clustering Algorithm

Description

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	The decay factor
Cm	Controls the threshold for dense grids
Cl	Controls the threshold for sparse grids
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:

• This implementation seems to use a 1 x 1 grid and therefore the range is increased in the example.

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

Author(s)

Matthias Carnein

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()

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

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

streamMOA documentation built on Sept. 4, 2022, 1:05 a.m.