Help for package streamMOA

Version:

1.3-1

Date:

2024-04-20

Encoding:

UTF-8

Title:

Interface for MOA Stream Clustering Algorithms

Description:

Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework (Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604).

Depends:

stream (≥ 2.0-0), rJava (≥ 1.0-1)

Imports:

graphics, stats, methods, proxy

Suggests:

RMOA (≥ 1.1.0)

SystemRequirements:

Java (>= 8)

BugReports:

https://github.com/mhahsler/streamMOA

License:

GPL-3

MOA code in inst/java/moa.jar is Copyright (C) The University of Waikato and distributed under the Apache License, version 2.0. All other code is Copyright (C) Matthew Bolanos, John Forrest and Michael Hahsler

RoxygenNote:

7.3.1

NeedsCompilation:

Packaged:

2024-04-20 18:10:21 UTC; hahsler

Author:

Michael Hahsler

[aut, cre, cph], John Forrest [aut, cph], Matthew Bolanos [ctb], Matthias Carnein [ctb], Dalibor Krleža [ctb]

Maintainer:

Michael Hahsler <mhahsler@lyle.smu.edu>

Repository:

CRAN

Date/Publication:

2024-04-20 18:30:02 UTC

streamMOA: Interface for MOA Stream Clustering Algorithms

Description

Author(s)

Maintainer: Michael Hahsler mhahsler@lyle.smu.edu (ORCID) [copyright holder]

Authors:

John Forrest [copyright holder]

Other contributors:

Matthew Bolanos [contributor]
Matthias Carnein [contributor]
Dalibor Krleža [contributor]

BICO - Fast computation of k-means coresets in a data stream

Description

This is an interface to the MOA implementation of BICO. The original BICO implementation by Fichtenberger et al is also available as stream::DSC_BICO.

Usage

DSC_BICO_MOA(
  Cluster = 5,
  Dimensions,
  MaxClusterFeatures = 1000,
  Projections = 10,
  k = NULL,
  space = NULL,
  p = NULL
)

Arguments

Cluster, k

Number of desired centers

Dimensions

The number of the dimensions of the input points (stream) need to be specified in advance

MaxClusterFeatures, space

Maximum size of the coreset

Projections, p

Number of random projections used for the nearest neighbor search

Details

BICO maintains a tree which is inspired by the clustering tree of BIRCH, a SIGMOD Test of Time award-winning clustering algorithm. Each node in the tree represents a subset of these points. Instead of storing all points as individual objects, only the number of points, the sum and the squared sum of the subset's points are stored as key features of each subset. Points are inserted into exactly one node.

Author(s)

Matthias Carnein

References

Hendrik Fichtenberger, Marc Gille, Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler: BICO: BIRCH Meets Coresets for k-Means Clustering. ESA 2013: 481-492

Examples

# data with 3 clusters and 2 dimensions
set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05)

# cluster with BICO
bico <- DSC_BICO_MOA(Cluster = 3, Dimensions = 2)
update(bico, stream, 100)
bico

# plot micro and macro-clusters
plot(bico, stream, type = "both")

CluStream Data Stream Clusterer

Description

Class implements the CluStream cluster algorithm for data streams (Aggarwal et al, 2003).

Usage

DSC_CluStream(m = 100, horizon = 1000, t = 2, k = 5)

Arguments

m

Defines the maximum number of micro-clusters used in CluStream

horizon

Defines the time window to be used in CluStream

t

Maximal boundary factor (i.e., the kernel radius factor). When deciding to add a new data point to a micro-cluster, the maximum boundary is defined as a factor of t of the RMS deviation of the data points in the micro-cluster from the centroid.

k

Number of macro-clusters to produce using weighted k-means.

Details

This is an interface to the MOA implementation of CluStream.

If k is specified, then CluStream applies a weighted k-means algorithm for reclustering (see Examples section below).

Value

An object of class DSC_CluStream (subclass of stream::DSC_Micro, DSC_MOA and stream::DSC).

Author(s)

Michael Hahsler and John Forrest

References

Aggarwal CC, Han J, Wang J, Yu PS (2003). "A Framework for Clustering Evolving Data Streams." In "Proceedings of the International Conference on Very Large Data Bases (VLDB '03)," pp. 81-92.

Bifet A, Holmes G, Pfahringer B, Kranen P, Kremer H, Jansen T, Seidl T (2010). MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering. In Journal of Machine Learning Research (JMLR).

Examples

# data with 3 clusters and 5% noise
set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 2, noise = .05)

# cluster with CluStream
clustream <- DSC_CluStream(m = 50, horizon = 100, k = 3)
update(clustream, stream, 500)
clustream

plot(clustream, stream, type = "both")

ClusTree Data Stream Clusterer

Description

Interface for the MOA implementation of the ClusTree data stream clustering algorithm (Kranen et al, 2009).

Usage

DSC_ClusTree(horizon = 1000, maxHeight = 8, lambda = NULL, k = NULL)

Arguments

horizon

Range of the (time) window.

maxHeight

The maximum height of the tree.

lambda

number used to override computed lambda (decay).

k

If specified, k-means with k clusters is used for reclustering.

Details

ClusTree uses a compact and self-adaptive index structure for maintaining stream summaries. Kranen et al (2009) suggest EM or k-means for reclustering.

Value

An object of class DSC_ClusTree (subclass of stream::DSC, DSC_MOA, stream::DSC_Micro).

Author(s)

Michael Hahsler and John Forrest

References

Philipp Kranen, Ira Assent, Corinna Baldauf, and Thomas Seidl. 2009. Self-Adaptive Anytime Stream Clustering. In Proceedings of the 2009 Ninth IEEE International Conference on Data Mining (ICDM '09). IEEE Computer Society, Washington, DC, USA, 249-258. doi:10.1109/ICDM.2009.47

Examples

# data with 3 clusters
set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05)

clustree <- DSC_ClusTree(maxHeight = 3)
update(clustree, stream, 500)
clustree

plot(clustree, stream)

#' Use automatically the k-means reclusterer with k = 3 to create macro clusters
clustree <- DSC_ClusTree(maxHeight = 3, k = 3)
update(clustree, stream, 500)
clustree

plot(clustree, stream, type = "both")

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.

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")

DenStream Data Stream Clusterer

Description

Interface for the DenStream cluster algorithm for data streams implemented in MOA.

Usage

DSC_DenStream(
  epsilon,
  mu = 1,
  beta = 0.2,
  lambda = 0.001,
  initPoints = 100,
  offline = 2,
  processingSpeed = 1,
  recluster = TRUE,
  k = NULL
)

Arguments

epsilon

defines the epsilon neighborhood which is the maximal radius of micro-clusters (r<=epsilon). Range: 0 to 1.

mu

minpoints as the weight w a core-micro-clusters needs to be created (w>=mu). Range: 0 to max(int).

beta

multiplier for mu to detect outlier micro-clusters given their weight w (w<beta x mu). Range: 0 to 1

lambda

decay constant.

initPoints

number of points to use for initialization via DBSCAN.

offline

offline multiplier for epsilon. Range: between 2 and 20). Used for reachability reclustering

processingSpeed

Number of incoming points per time unit (important for decay). Range: between 1 and 1000.

recluster

logical; should the offline DBSCAN-based (i.e., reachability at a distance of epsilon) be performed?

k

integer; tries to automatically chooses offline to find k macro-clusters.

Details

DenStream applies reachability (from DBSCAN) between micro-clusters for reclustering using epsilon x offline (defaults to 2) as the reachability threshold.

If k is specified it automatically chooses the reachability threshold to find k clusters. This is achieved using single-link hierarchical clustering.

Value

An object of class DSC_DenStream (subclass of stream::DSC, DSC_MOA, stream::DSC_Micro) or, for recluster = TRUE, an object of class stream::DSC_TwoStage.

Author(s)

Michael Hahsler and John Forrest

References

Cao F, Ester M, Qian W, Zhou A (2006). Density-Based Clustering over an Evolving Data Stream with Noise. In Proceedings of the 2006 SIAM International Conference on Data Mining, pp 326-337. SIAM.

Examples

# data with 3 clusters and 5% noise
set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05)

# use Den-Stream with reachability reclustering
denstream <- DSC_DenStream(epsilon = .05)
update(denstream, stream, 500)
denstream

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

# plot micro-cluster
plot(denstream, stream, type = "micro")

# show micro and macro-clusters
plot(denstream, stream, type = "both")

# reclustering: Choose reclustering reachability threshold automatically to find 4 clusters
denstream2 <- DSC_DenStream(epsilon = .05, k = 4)
update(denstream2, stream, 500)
plot(denstream2, stream, type = "both")

Micro-cluster Continuous Outlier Detector (MCOD)

Description

Class interfaces the MOA implementation of the MCOD algorithm for distance-based data stream outlier detection.

Usage

DSC_MCOD(r = 0.1, t = 50, w = 1000, recheck_outliers = FALSE)

DSOutlier_MCOD(r = 0.1, t = 50, w = 1000, recheck_outliers = TRUE)

get_outlier_positions(x, ...)

recheck_outlier(x, outlier_correlated_id, ...)

clean_outliers(x, ...)

Arguments

r

Defines the micro-cluster radius.

t

Defines the number of neighbors (k in the article).

w

Defines the window width in data points.

recheck_outliers

Defines that the MCOD algorithm allows re-checking of detected outliers.

x

a DSC_MCOD object.

...

further arguments are currently ignored.

outlier_correlated_id

ids of outliers.

Details

The algorithm detects density-based outliers. An object x is defined to be an outlier if there are less than t objects lying at distance at most r from x.

Outliers are stored and can be retrieved using get_outlier_position() and recheck_outlier().

Note: The implementation updates the clustering when predict() is called.

Value

An object of class DSC_MCOD (subclass of stream::DSC_Micro, DSC_MOA and stream::DSC).

Functions

get_outlier_positions(): Returns spatial positions of all current outliers.
recheck_outlier(): DSC_MCOD Re-checks the outlier having outlier_correlated_id. If this object is still an outlier, the method returns TRUE.
clean_outliers(): forget detected outliers from the outlier detector (currently not implemented).

Author(s)

Dalibor Krleža

References

Kontaki M, Gounaris A, Papadopoulos AN, Tsichlas K, and Manolopoulos Y (2016). Efficient and flexible algorithms for monitoring distance-based outliers over data streams. Information Systems, Vol. 55, pp. 37-53. doi:10.1109/ICDE.2011.5767923

Examples

# Example 1: Clustering with MCOD
stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05)
mcod <- DSC_MCOD(r = .1, t = 3, w = 100)
update(mcod, stream, 100)
mcod

plot(mcod, stream, n = 100)

# Example 2: Predict outliers (have a class label of NA)
stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05)
mcod <- DSOutlier_MCOD(r = .1, t = 3, w = 100)
update(mcod, stream, 100)

plot(mcod, stream, n = 100)

# MCOD can retried the outliers
get_outlier_positions(mcod)

# Example 3: evaluate on a stream
evaluate_static(mcod, stream, n = 100, type = "micro",
  measure = c("crand", "noisePrecision", "outlierjaccard"))

DSC_MOA Class

Description

An abstract class that inherits from the base class stream::DSC and provides the common functions needed to interface MOA clusterers.

Usage

DSC_MOA(...)

Arguments

...

further arguments.

Details

DSC_MOA is a subclass of stream::DSC for MOA-based clusterers. DSC_MOA classes operate in a different way in that the centers of the micro-clusters have to be extracted from the underlying Java object. This is done by using rJava to perform method calls directly in the JRI and converting the multi-dimensional Java array into a local R data type.

Note: The formula interface is currently not implemented for MOA-based clusterers. Use stream::DSF to select features instead.

Author(s)

Michael Hahsler and John Forrest

References

Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604

streamKM++

Description

This is an interface to the MOA implementation of streamKM++.

Usage

DSC_StreamKM(sizeCoreset = 10000, numClusters = 5, length = 100000L, ...)

Arguments

sizeCoreset

Size of the coreset

numClusters

Number of clusters to compute

length

Length of the data stream

...

Further arguments ignored.

Details

streamKM++ uses a tree-based sampling strategy to obtain a small weighted sample of the stream called coreset. The MOA implementation applies the k-means++ algorithm to find a given number of centers in the coreset.

Notes:

The cluster can only cluster the number of points specified in length ans then produces an ArrayIndexOutOfBoundsException error.
The coreset (micro-clusters are not accessible), only the macro-clusters can be requested.

Author(s)

Matthias Carnein

References

Marcel R. Ackermann, Christiane Lammersen, Marcus Maertens, Christoph Raupach, Christian Sohler, Kamil Swierkot. StreamKM++: A Clustering Algorithm for Data Streams. In: Proceedings of the 12th Workshop on Algorithm Engineering and Experiments (ALENEX '10), 2010.

Examples

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

# cluster with streamKM++
streamkm <- DSC_StreamKM(sizeCoreset = 100, numClusters = 3, length = 1000)
update(streamkm, stream, 100)
streamkm

# plot macro-clusters (no access to micro-clusters)
plot(streamkm, stream)

DSClassifier_MOA – MOA-based Stream Classifiers

Description

Interface for MOA-based stream classification methods based on package RMOA.

Usage

DSClassifier_MOA(formula, RMOA_classifier)

## S3 method for class 'DSClassifier_MOA'
update(object, dsd, n = 1, verbose = FALSE, block = 1000L, ...)

## S3 method for class 'DSClassifier_MOA'
predict(object, newdata, type = "response", ...)

Arguments

formula

a formula for the classification problem.

RMOA_classifier

a RMOA_classifier object.

object

a DSC object.

dsd

a data stream object.

n

number of data points taken from the stream.

verbose

logical; show progress?

block

process blocks of data to improve speed.

...

further arguments.

newdata

dataframe with the new data.

type

prediction type (see RMOA::predict.MOA_trainedmodel()).

Details

DSClassifier_MOA provides an interface to MOA-based stream classifiers using package RMOA. RMOA provides access to MOAs stream classifiers in the following groups:

Subsequent calls to update() update the current model.

Value

An object of class DSClassifier_MOA

Author(s)

Michael Hahsler

References

Wijffels, J. (2014) Connect R with MOA to perform streaming classifications. https://github.com/jwijffels/RMOA

Bifet A, Holmes G, Pfahringer B, Kranen P, Kremer H, Jansen T, Seidl T (2010). MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering. Journal of Machine Learning Research (JMLR).

Examples

## Not run: 
library(streamMOA)
library(RMOA)

# create a data stream for the iris dataset
data <- iris[sample(nrow(iris)), ]
stream <- DSD_Memory(data)
stream

# define the stream classifier. MOAmodelOptions can be passed on as a control parameter
#   to the call RMOA::HoeffdingTree(). See ? RMOA::MOAoptions
cl <- DSClassifier_MOA(
  Species ~ Sepal.Length + Sepal.Width + Petal.Length,
  RMOA::HoeffdingTree()
  )

cl

# update the classifier with 100 points from the stream
update(cl, stream, 100)

# look at the classifier RMOA object
cl$RMOAObj

# predict the class for the next 50 points
newdata <- get_points(stream, n = 50)
pr <- predict(cl, newdata)
pr

table(pr, newdata$Species)

## End(Not run)

Base class for MOA-based Data Stream Generators

Description

Abstract base class for MOA-based data stream generators directly inherits from stream::DSD.

Usage

DSD_MOA(...)

Arguments

...

further arguments.

Value

The abstract class cannot be instantiated and produces an error.

Author(s)

Michael Hahsler

References

MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl. Journal of Machine Learning Research (JMLR).

Examples

DSD()

Random RBF Generator Events Data Stream Generator

Description

A class that generates random data based on RandomRBFGeneratorEvents implemented in MOA.

Usage

DSD_RandomRBFGeneratorEvents(
  k = 3,
  d = 2,
  numClusterRange = 3L,
  kernelRadius = 0.07,
  kernelRadiusRange = 0,
  densityRange = 0,
  speed = 100L,
  speedRange = 0L,
  noiseLevel = 0.1,
  noiseInCluster = FALSE,
  eventFrequency = 30000L,
  eventMergeSplitOption = FALSE,
  eventDeleteCreate = FALSE,
  modelSeed = NULL,
  instanceSeed = NULL
)

Arguments

k

The average number of centroids in the model.

d

The dimensionality of the data.

numClusterRange

Range for number of clusters.

kernelRadius

The average radius of the micro-clusters.

kernelRadiusRange

Deviation of the number of centroids in the model.

densityRange

Density range.

speed

Kernels move a predefined distance of 0.01 every X points.

speedRange

Speed/Velocity point offset.

noiseLevel

Noise level.

noiseInCluster

Allow noise to be placed within a cluster.

eventFrequency

Frequency of events.

eventMergeSplitOption

Merge and split?

eventDeleteCreate

Delete and create?

modelSeed

Random seed for the model.

instanceSeed

Random seed for the instances.

Details

There are an assortment of parameters available for the underlying MOA data structure, however, we have currently limited the available parameters to the arguments above. Currently the modelSeed and instanceSeed are set to default values every time a DSD_MOA is created, therefore the generated data will be the same. Because of this, it is important to set the seed manually when different data is needed.

The default behavior is to create a data stream with 3 clusters and concept drift. The locations of the clusters will change slightly, and they will merge with one another as time progresses.

Value

An object of class DSD_RandomRBFGeneratorEvent (subclass of DSD_MOA, stream::DSD).

Author(s)

Michael Hahsler and John Forrest

References

Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl. MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering Journal of Machine Learning Research (JMLR), 2010.

Examples

stream <- DSD_RandomRBFGeneratorEvents()
get_points(stream, 10)

if (interactive()) {
animate_data(stream, n = 5000, horizon = 100, xlim = c(0, 1), ylim = c(0, 1))
}

DSRegressor_MOA – MOA-based Stream Regressors

Description

Interface for MOA-based stream regression methods based on package RMOA.

Usage

DSRegressor_MOA(formula, RMOA_regressor)

## S3 method for class 'DSRegressor_MOA'
update(object, dsd, n = 1, verbose = FALSE, block = 1000L, ...)

## S3 method for class 'DSRegressor_MOA'
predict(object, newdata, type = "response", ...)

Arguments

formula

a formula for the regression problem.

RMOA_regressor

a RMOA_regressors object.

object

a DSC object.

dsd

a data stream object.

n

number of data points taken from the stream.

verbose

logical; show progress?

block

process blocks of data to improve speed.

...

further arguments.

newdata

dataframe with the new data.

type

prediction type (see RMOA::predict.MOA_trainedmodel()).

Details

DSRegressor_MOA provides an interface to MOA-based stream regressors using package RMOA. Available regressors can be found at RMOA::MOA_regressors.

Subsequent calls to update() update the current model.

Value

An object of class DSRegressor_MOA

Author(s)

Michael Hahsler

References

Wijffels, J. (2014) Connect R with MOA to perform streaming classifications. https://github.com/jwijffels/RMOA

Examples

## Not run: 
library(streamMOA)
library(RMOA)

# create a data stream for the iris dataset
data <- iris[sample(nrow(iris)), ]
stream <- DSD_Memory(data)
stream

# define a stream regression model.
cl <- DSRegressor_MOA(
  Sepal.Length ~ Species + Sepal.Width + Petal.Length,
  RMOA::Perceptron()
  )

cl

# update the model with 100 points from the stream
update(cl, stream, 100)

# look at the RMOA model object
cl$RMOAObj

# make predictions for the next 50 points
newdata <- get_points(stream, n = 50)
pr <- predict(cl, newdata)
pr

plot(pr, newdata$Sepal.Length, xlim = c(0,10), ylim = c(0,10))
abline(a = 0, b = 1, col = "red")

## End(Not run)

streamMOA: Interface for MOA Stream Clustering Algorithms

Description

Author(s)

See Also

BICO - Fast computation of k-means coresets in a data stream

Description

Usage

Arguments

Details

Author(s)

References

See Also

Examples

CluStream Data Stream Clusterer

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

ClusTree Data Stream Clusterer

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

D-Stream Data Stream Clustering Algorithm

Description

Usage

Arguments

Details

Author(s)

References

See Also

Examples

DenStream Data Stream Clusterer

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Micro-cluster Continuous Outlier Detector (MCOD)

Description

Usage

Arguments

Details

Value

Functions

Author(s)

References

See Also

Examples

DSC_MOA Class

Description

Usage

Arguments

Details

Author(s)

References

See Also

streamKM++

Description

Usage

Arguments

Details

Author(s)

References

See Also

Examples