Getting started with streamMOA
In streamMOA: Interface for MOA Stream Clustering Algorithms

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(streamMOA)
library(stream)
set.seed(1234)

The streamMOA package connects stream to clustering algorithms from MOA (Massive Online Analysis). This vignette shows how to create a data stream, process it with a MOA clusterer, and inspect the resulting micro- and macro-clusters. Java 8 or later is required.

Installation

Install the package from CRAN and load it together with stream:

install.packages(c("stream", "streamMOA"))

library(streamMOA)
library(stream)

Create a data stream

DSD_Gaussians() from stream generates a stream with three clusters and a small amount of noise. Request a finite number of observations with get_points():

stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05)
get_points(stream, n = 5)

Stream observations are generated as requested, so plotting or retrieving points advances the stream. Set info = FALSE when only feature values are needed and cluster labels are not required.

plot(stream, n = 500)

Cluster the stream

DSC_CluStream() creates a MOA-based online clusterer. The m parameter sets the maximum number of micro-clusters, while horizon controls the time window. The optional k argument enables weighted k-means reclustering to produce macro-clusters. Use update() to train the clusterer on incoming observations:

clustering <- DSC_CluStream(m = 50, horizon = 100, k = 3)
update(clustering, stream, n = 500)
clustering

Inspect the result

The micro-clusters summarize recent observations. Macro-clusters group those summaries into a smaller number of clusters. Retrieve their centers or plot them with the stream:

get_microclusters(clustering)
get_macroclusters(clustering)
plot(clustering, stream, type = "both")

Other clusterers include DSC_DenStream(), DSC_ClusTree(), DSC_DStream_MOA(), DSC_BICO_MOA(), and DSC_StreamKM(). See their help pages and the package vignette Introduction to streamMOA for algorithm details, options, and additional examples.