R package stream - Infrastructure for Data Stream Mining R package stream - Infrastructure for Data Stream MiningMaintainer: Michael Hahsler
The package provides support for modeling and simulating data streams as well as an extensible framework for implementing, interfacing and experimenting with algorithms with a focus on data stream clustering tasks. The main advantage of stream is that it seamlessly integrates with the large existing infrastructure provided by R. The package provides:
Additional packages in the stream family are:
To cite package ‘stream’ in publications use:
Hahsler M, Bolaños M, Forrest J (2017). “Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R.” Journal of Statistical Software, 76(14), 1-50. doi:10.18637/jss.v076.i14 https://doi.org/10.18637/jss.v076.i14.
@Article{,
title = {Introduction to {stream}: An Extensible Framework for Data Stream Clustering Research with {R}},
author = {Michael Hahsler and Matthew Bola{\~n}os and John Forrest},
journal = {Journal of Statistical Software},
year = {2017},
volume = {76},
number = {14},
pages = {1--50},
doi = {10.18637/jss.v076.i14},
}
Stable CRAN version: Install from within R with
install.packages("stream")
Current development version: Install from r-universe.
install.packages("stream",
repos = c("https://mhahsler.r-universe.dev",
"https://cloud.r-project.org/"))
Load the package and a random data stream with 3 Gaussian clusters and 10% noise and scale the data to z-scores.
library("stream")
set.seed(2000)
stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.1) %>%
DSF_Scale()
get_points(stream, n = 5)
## X1 X2 .class
## 1 -0.267 -0.802 2
## 2 0.531 1.078 NA
## 3 -0.706 1.427 3
## 4 -0.781 1.355 3
## 5 1.170 -0.712 1
plot(stream)
Cluster a stream of 1000 points using D-Stream which estimates point density in grid cells.
dsc <- DSC_DStream(gridsize = 0.1)
update(dsc, stream, 1000)
plot(dsc, stream, grid = TRUE)
evaluate_static(dsc, stream, n = 100)
## Evaluation results for micro-clusters.
## Points were assigned to micro-clusters.
##
## numPoints numMicroClusters numMacroClusters
## 100.0000 65.0000 3.0000
## noisePredicted SSQ silhouette
## 23.0000 0.1696 0.0786
## average.between average.within max.diameter
## 1.7809 0.5816 3.9368
## min.separation ave.within.cluster.ss g2
## 0.0146 0.5217 0.1596
## pearsongamma dunn dunn2
## 0.0637 0.0037 0.0154
## entropy wb.ratio numClasses
## 3.1721 0.3266 4.0000
## noiseActual noisePrecision outlierJaccard
## 16.0000 0.6957 0.6957
## precision recall F1
## 0.6170 0.1618 0.2563
## purity Euclidean Manhattan
## 0.9920 0.1633 0.3000
## Rand cRand NMI
## 0.7620 0.1688 0.5551
## KP angle diag
## 0.2651 0.3000 0.3000
## FM Jaccard PS
## 0.3159 0.1470 0.0541
## vi
## 2.2264
## attr(,"type")
## [1] "micro"
## attr(,"assign")
## [1] "micro"
Outlier detection using DBSTREAM which uses micro-clusters with a given radius.
dso <- DSOutlier_DBSTREAM(r = 0.1)
update(dso, stream, 1000)
plot(dso, stream)
evaluate_static(dso, stream, n = 100, measure = c("numPoints", "noiseActual", "noisePredicted",
"noisePrecision"))
## Evaluation results for micro-clusters.
## Points were assigned to micro-clusters.
##
## numPoints noiseActual noisePredicted noisePrecision
## 100 7 7 1
## attr(,"type")
## [1] "micro"
## attr(,"assign")
## [1] "micro"
Preparing complete stream process pipelines that can be run using a
single update() call.
pipeline <- DSD_Gaussians(k = 3, d = 2, noise = 0.1) %>%
DSF_Scale() %>%
DST_Runner(DSC_DStream(gridsize = 0.1))
pipeline
## DST pipline runner
## DSD: Gaussian Mixture (d = 2, k = 3)
## + scaled
## DST: D-Stream
## Class: DST_Runner, DST
update(pipeline, n = 500)
pipeline$dst
## D-Stream
## Class: DSC_DStream, DSC_Micro, DSC_R, DSC
## Number of micro-clusters: 160
## Number of macro-clusters: 13
The development of the stream package was supported in part by NSF IIS-0948893, NSF CMMI 1728612, and NIH R21HG005912.