Create a Data Stream Pipeline

Define a complete data stream pipe line consisting of a data stream, filters and a data mining task using %>%.

Usage

DST_Runner(dsd, dst)

Arguments

dsd

A data stream (subclass of DSD) typically provided using a %>% (pipe).

dst

A data stream mining task (subclass of DST).

Details

A data stream pipe line consisting of a data stream, filters and a data mining task:

DSD %>% DSF %>% DST_Runner

Once the pipeline is defined, it can be run using update() where points are taken from the DSD data stream source, filtered through a sequence of DSF filters and then used to update the DST task.

DST_Multi can be used to update multiple models in the pipeline with the same stream.

See also

Other DST: DSAggregate(), DSC(), DSClassifier(), DSOutlier(), DSRegressor(), DST(), DST_SlidingWindow(), DST_WriteStream(), evaluate, predict, update

Author

Michael Hahsler

Examples

set.seed(1500)

# Set up a pipeline with a DSD data source, DSF Filters and then a DST task
cluster_pipeline <- DSD_Gaussians(k = 3, d = 2) %>%
                    DSF_Scale() %>%
                    DST_Runner(DSC_DBSTREAM(r = .3))

cluster_pipeline
#> DST pipline runner
#> DSD: Gaussian Mixture (d = 2, k = 3)
#> + scaled
#> DST: DBSTREAM 
#> Class: DST_Runner, DST 

# the DSD and DST can be accessed directly
cluster_pipeline$dsd
#> Gaussian Mixture (d = 2, k = 3)
#> + scaled 
#> Class: DSF_Scale, DSF, DSD_R, DSD 
cluster_pipeline$dst
#> DBSTREAM 
#> Class: DSC_DBSTREAM, DSC_Micro, DSC_R, DSC 
#> Number of micro-clusters: 0 
#> Number of macro-clusters: 0 

# update the DST using the pipeline, by default update returns the micro clusters
update(cluster_pipeline, n = 1000)

cluster_pipeline$dst
#> DBSTREAM 
#> Class: DSC_DBSTREAM, DSC_Micro, DSC_R, DSC 
#> Number of micro-clusters: 33 
#> Number of macro-clusters: 3 
get_centers(cluster_pipeline$dst, type = "macro")
#>           X1         X2
#> 1  0.8247073  1.1892657
#> 2  0.3829904 -0.7893955
#> 3 -1.5545524 -0.7060603
plot(cluster_pipeline$dst)