,
John Forrest [aut, cph],
Matthew Bolanos [ctb],
Matthias Carnein [ctb],
Dalibor Krleža [ctb]| 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). |
| Authors: | Michael Hahsler [aut, cre, cph]
,
John Forrest [aut, cph],
Matthew Bolanos [ctb],
Matthias Carnein [ctb],
Dalibor Krleža [ctb] |
| Maintainer: | Michael Hahsler <[email protected]> |
| License: | GPL-3 |
| Version: | 1.3-1 |
| Built: | 2024-11-25 05:09:40 UTC |
| Source: | https://github.com/mhahsler/streamMOA |
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.
DSC_BICO_MOA( Cluster = 5, Dimensions, MaxClusterFeatures = 1000, Projections = 10, k = NULL, space = NULL, p = NULL )DSC_BICO_MOA( Cluster = 5, Dimensions, MaxClusterFeatures = 1000, Projections = 10, k = NULL, space = NULL, p = NULL )
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 |
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.
Matthias Carnein
Hendrik Fichtenberger, Marc Gille, Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler: BICO: BIRCH Meets Coresets for k-Means Clustering. ESA 2013: 481-492
Other DSC_MOA:
DSC_CluStream(),
DSC_ClusTree(),
DSC_DStream_MOA(),
DSC_DenStream(),
DSC_MCOD(),
DSC_MOA(),
DSC_StreamKM()
# 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")# 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")
Class implements the CluStream cluster algorithm for data streams (Aggarwal et al, 2003).
DSC_CluStream(m = 100, horizon = 1000, t = 2, k = 5)DSC_CluStream(m = 100, horizon = 1000, t = 2, k = 5)
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 |
k |
Number of macro-clusters to produce using weighted k-means. |
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).
An object of class DSC_CluStream (subclass of
stream::DSC_Micro, DSC_MOA and stream::DSC).
Michael Hahsler and John Forrest
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).
Other DSC_MOA:
DSC_BICO_MOA(),
DSC_ClusTree(),
DSC_DStream_MOA(),
DSC_DenStream(),
DSC_MCOD(),
DSC_MOA(),
DSC_StreamKM()
# 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")# 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")
Interface for the MOA implementation of the ClusTree data stream clustering algorithm (Kranen et al, 2009).
DSC_ClusTree(horizon = 1000, maxHeight = 8, lambda = NULL, k = NULL)DSC_ClusTree(horizon = 1000, maxHeight = 8, lambda = NULL, k = NULL)
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. |
ClusTree uses a compact and self-adaptive index structure for maintaining stream summaries. Kranen et al (2009) suggest EM or k-means for reclustering.
An object of class DSC_ClusTree (subclass of stream::DSC,
DSC_MOA, stream::DSC_Micro).
Michael Hahsler and John Forrest
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
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).
Other DSC_MOA:
DSC_BICO_MOA(),
DSC_CluStream(),
DSC_DStream_MOA(),
DSC_DenStream(),
DSC_MCOD(),
DSC_MOA(),
DSC_StreamKM()
# 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")# 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")
Interface for the DenStream cluster algorithm for data streams implemented in MOA.
DSC_DenStream( epsilon, mu = 1, beta = 0.2, lambda = 0.001, initPoints = 100, offline = 2, processingSpeed = 1, recluster = TRUE, k = NULL )DSC_DenStream( epsilon, mu = 1, beta = 0.2, lambda = 0.001, initPoints = 100, offline = 2, processingSpeed = 1, recluster = TRUE, k = NULL )
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. |
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.
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.
Michael Hahsler and John Forrest
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.
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).
Other DSC_MOA:
DSC_BICO_MOA(),
DSC_CluStream(),
DSC_ClusTree(),
DSC_DStream_MOA(),
DSC_MCOD(),
DSC_MOA(),
DSC_StreamKM()
# 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")# 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")
This is an interface to the MOA implementation of D-Stream. A C++ implementation (including reclustering with attraction) is available as stream::DSC_DStream.
DSC_DStream_MOA(decayFactor = 0.998, Cm = 3, Cl = 0.8, Beta = 0.3)DSC_DStream_MOA(decayFactor = 0.998, Cm = 3, Cl = 0.8, Beta = 0.3)
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 |
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.
Matthias Carnein
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.
Other DSC_MOA:
DSC_BICO_MOA(),
DSC_CluStream(),
DSC_ClusTree(),
DSC_DenStream(),
DSC_MCOD(),
DSC_MOA(),
DSC_StreamKM()
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")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")
Class interfaces the MOA implementation of the MCOD algorithm for distance-based data stream outlier detection.
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, ...)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, ...)
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 |
... |
further arguments are currently ignored. |
outlier_correlated_id |
ids of outliers. |
The algorithm detects density-based outliers. An object is defined
to be an outlier if there are less than objects lying at distance at
most from .
Outliers are stored and can be retrieved using get_outlier_position() and
recheck_outlier().
Note: The implementation updates the clustering when predict() is called.
An object of class DSC_MCOD (subclass of
stream::DSC_Micro, DSC_MOA and stream::DSC).
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).
Dalibor Krleža
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
Other DSC_MOA:
DSC_BICO_MOA(),
DSC_CluStream(),
DSC_ClusTree(),
DSC_DStream_MOA(),
DSC_DenStream(),
DSC_MOA(),
DSC_StreamKM()
# 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"))# 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"))
An abstract class that inherits from the base class stream::DSC and provides the common functions needed to interface MOA clusterers.
DSC_MOA(...)DSC_MOA(...)
... |
further arguments. |
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.
Michael Hahsler and John Forrest
Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604
Other DSC_MOA:
DSC_BICO_MOA(),
DSC_CluStream(),
DSC_ClusTree(),
DSC_DStream_MOA(),
DSC_DenStream(),
DSC_MCOD(),
DSC_StreamKM()
This is an interface to the MOA implementation of streamKM++.
DSC_StreamKM(sizeCoreset = 10000, numClusters = 5, length = 100000L, ...)DSC_StreamKM(sizeCoreset = 10000, numClusters = 5, length = 100000L, ...)
sizeCoreset |
Size of the coreset |
numClusters |
Number of clusters to compute |
length |
Length of the data stream |
... |
Further arguments ignored. |
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.
Matthias Carnein
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.
Other DSC_MOA:
DSC_BICO_MOA(),
DSC_CluStream(),
DSC_ClusTree(),
DSC_DStream_MOA(),
DSC_DenStream(),
DSC_MCOD(),
DSC_MOA()
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)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)
Interface for MOA-based stream classification methods based on package RMOA.
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", ...)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", ...)
formula |
a formula for the classification problem. |
RMOA_classifier |
a |
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 |
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.
An object of class DSClassifier_MOA
Michael Hahsler
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).
## 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)## 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)
Abstract base class for MOA-based data stream generators directly inherits from stream::DSD.
DSD_MOA(...)DSD_MOA(...)
... |
further arguments. |
The abstract class cannot be instantiated and produces an error.
Michael Hahsler
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).
Other DSD_MOA:
DSD_RandomRBFGeneratorEvents()
DSD()DSD()
A class that generates random data based on RandomRBFGeneratorEvents implemented in MOA.
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 )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 )
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. |
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.
An object of class DSD_RandomRBFGeneratorEvent (subclass of
DSD_MOA, stream::DSD).
Michael Hahsler and John Forrest
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.
Other DSD_MOA:
DSD_MOA()
stream <- DSD_RandomRBFGeneratorEvents() get_points(stream, 10) if (interactive()) { animate_data(stream, n = 5000, horizon = 100, xlim = c(0, 1), ylim = c(0, 1)) }stream <- DSD_RandomRBFGeneratorEvents() get_points(stream, 10) if (interactive()) { animate_data(stream, n = 5000, horizon = 100, xlim = c(0, 1), ylim = c(0, 1)) }
Interface for MOA-based stream regression methods based on package RMOA.
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", ...)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", ...)
formula |
a formula for the regression problem. |
RMOA_regressor |
a |
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 |
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.
An object of class DSRegressor_MOA
Michael Hahsler
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).
## 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)## 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)