\input{head.tex}

%\VignetteIndexEntry{Introduction to streamMOA}

<<echo=FALSE>>=
options(width = 75, digits = 3, prompt = 'R> ')
@

\section{Introduction}
Please refer to the vignette in package~\pkg{stream} for an introduction
to data stream mining in \proglang{R}. In this vignette we give two examples
that show how to use the \pkg{stream} framework being used from start to finish.
The examples encompasses the creation of data streams, preparation of
data stream clustering algorithms, the online
clustering of data points into micro-clusters, reclustering and finally
evaluation.
The first example shows how compare a set of data stream clustering
algorithms on a static data set. The second example shows how to perform
evaluation on a data stream with concept drift (clusters evolve over time).

\section{Experimental Comparison on Static Data} \label{examples:full}

First, we set up a static data set.
We extract 1500 data points from the Bars and Gaussians data stream generator
with 5\% noise and put them in a \code{DSD_Memory}. The wrapper is used
to replay the same part of the data stream for each algorithm.
We will use the first
1000 points to learn the clustering and the remaining 500 points for
evaluation.

<<echo=FALSE>>=
set.seed(1234)
@

<<data_bng, fig=TRUE, include=FALSE>>=
library("stream")
stream <- DSD_Memory(DSD_BarsAndGaussians(noise=0.05), n=5500)
stream
plot(stream)
@

\begin{figure}
\centering
\includegraphics[width=.5\linewidth]{streamMOA-data_bng}
\caption{Bar and Gaussians data set.}
\label{figure:data_bng}
\end{figure}

Figure~\ref{figure:data_bng} shows the structure of the data set.
It consists of four clusters,
two Gaussians and two uniformly filled rectangular clusters. The Gaussian and the
bar to the right have $1/3$ the density of the other two clusters.

We initialize four algorithms from \pkg{stream}. We choose the parameters
experimentally so that the algorithm produce each (approximately) 100 micro-clusters.

<<>>=
sample <- DSC_TwoStage(micro=DSC_Sample(k=100), macro=DSC_Kmeans(k=4))
window <- DSC_TwoStage(micro=DSC_Window(horizon=100), macro=DSC_Kmeans(k=4))
dstream <- DSC_DStream(gridsize=.7)
dbstream <- DSC_DBSTREAM(r=.45)
@

We will also use two MOA-based algorithms available in package~\pkg{streamMOA}.

<<>>=
library("streamMOA")
denstream <- DSC_DenStream_MOA(epsilon=.5, mu=1)
clustream <- DSC_CluStream_MOA(m=100, k=4)
@

We store the algorithms in a list for easier handling and then cluster the same
1000 data points with each algorithm. Note that we have to reset the stream
each time before we cluster.

<<>>=
algorithms <- list(Sample=sample, Window=window, 'D-Stream'=dstream,
  DBSTREAM=dbstream, DenStream_MOA=denstream, CluStream_MOA=clustream)
for(a in algorithms) {
  reset_stream(stream)
  update(a, stream, 5000)
}
@

We use \code{nclusters()} to inspect the number of micro-clusters.
<<>>=
sapply(algorithms, nclusters, type="micro")
@

All algorithms except DenStream produce around 100 micro-clusters.
We were not able to adjust DenStream to produce more than around 50 micro-clusters
for this data set.

To inspect micro-cluster placement, we plot the calculated micro-clusters and the
original data.

<<microclusters, fig=TRUE, include=FALSE, width=8, height=12>>=
op <- par(no.readonly = TRUE)
layout(mat=matrix(1:6, ncol=2))
for(a in algorithms) {
  reset_stream(stream)
  plot(a, stream, main=description(a), type="micro")
}
par(op)
@

\begin{figure}
\centering
\includegraphics{streamMOA-microclusters}
\caption{Micro-cluster placement for different data stream clustering
algorithms.}
\label{figure:microclusters}
\end{figure}

Figure~\ref{figure:microclusters} shows the micro-cluster placement by
the different algorithms. Micro-clusters are shown as red circles and
the size is proportional to each cluster's weight.
Reservoir sampling and the sliding window randomly place the micro-clusters
and also a few noise points (shown as grey dots).
Clustream also does not suppress noise and
places even more micro-clusters on noise points since it tries to
represent all data as faithfully as possible. D-Stream, DenStream and DBSTREAM all
suppress noise and concentrate the micro-clusters on the real clusters. D-Stream
is grid-based and thus the micro-clusters are regularly spaced. DBSTREAM
produces a similar, almost regular pattern. DenStream
produces one heavy micro-cluster on one cluster, while using a large number of
micro clusters for the others. It also has problems with detecting the
rectangular low-density cluster.

It is also interesting to compare the assignment areas for micro-clusters
created by different algorithms. The assignment area is the area around
the center of a micro-cluster in which points are considered to belong to
the micro-cluster. In case that a point is in the assignment area of several
micro-clusters, the closer center is chosen.
To show the assignment area we add \code{assignment=TRUE} to plot.
We also disable showing micro-cluster weights to make the plot clearer.

<<microclusters_assignment, fig=TRUE, include=FALSE, width=8, height=12>>=
op <- par(no.readonly = TRUE)
layout(mat=matrix(1:6, ncol=2))
for(a in algorithms) {
  reset_stream(stream)
  plot(a, stream, main=description(a), assignment=TRUE, weight=FALSE, type="micro")
}
par(op)
@

\begin{figure}
\centering
\includegraphics{streamMOA-microclusters_assignment}
\caption{Micro-cluster assignment areas for different data stream clustering
algorithms.}
\label{figure:microclusters_assignment}
\end{figure}

Figure~\ref{figure:microclusters_assignment} shows the assignment areas as dotted
circles around micro-clusters. Reservoir sampling and sliding window does
not provide assignment areas and data points are always assigned to
the nearest micro-cluster. D-Stream is grid-based
and shows the assignment area as grey boxes. DBSTREAM uses the same
radius for all micro-clusters, while DenStream and CluStream calculate
the assignment area for each micro-cluster.

To compare the cluster quality, we can check for example the micro-cluster purity, the
sum of squares and the average silhouette coefficient. Note that we reset the
stream to position 1001 since we have used the first 1000 points for learning and
we want to use data points not seen by the algorithms for evaluation.

<<>>=
sapply(algorithms, FUN=function(a) {
  reset_stream(stream, 1001)
  evaluate(a, stream,
    measure=c("numMicroClusters", "purity", "SSQ", "silhouette"),
    n=500, assignmentMethod = "auto", type = "micro")
})
@

We need to be careful with the comparison of these numbers, since the depend
heavily on the number of micro-clusters with more clusters leading to a
better value. Therefore, a comparison with DenStream
is not valid.
We can compare the measures, of the other algorithms since the number
of micro-clusters is close.
Sampling and the sliding window produce very good values
for purity, CluStream achieves the highest average
silhouette coefficient and DBSTREAM produces the lowest sum of squares.
For better results more data and cross-validation could be used.

Next, we compare macro-clusters. D-Stream, DenStream, DBSTREAM and CluStream
have built-in reclustering strategies.
D-Stream joins adjacent dense grid cells for form macro-clusters.
DenStream and DBSTREAM use the reachability concept (from DBSCAN).
CluStream used weighted $k$-means clustering (note that we used
$k=4$ when we initialized \code{DSC_DenStream} above).
For sampling and window we apply here weighted
$k$-means reclustering with $k=4$, the true number of clusters.


<<macroclusters, fig=TRUE, include=FALSE, width=8, height=12>>=
op <- par(no.readonly = TRUE)
layout(mat=matrix(1:6, ncol=2))
for(a in algorithms) {
  reset_stream(stream)
  plot(a, stream, main=description(a), type="both")
}
par(op)
@

\begin{figure}
\centering
\includegraphics{streamMOA-macroclusters}
\caption{Macro-cluster placement for different data stream clustering algorithms}
\label{figure:macroclusters}
\end{figure}

Figure~\ref{figure:macroclusters} shows the macro-cluster placement. Sample, window and
CluStream use $k$-means reclustering and therefore produce exactly four clusters.
However, the placement is off, splitting a true cluster and missing one of the less
dense clusters.
DenStream, DBSTREAM and D-Stream identify the two denser clusters
correctly, but split the lower density clusters into multiple pieces.

<<>>=
sapply(algorithms, FUN=function(a) {
  reset_stream(stream, 1001)
  evaluate(a, stream, measure = c("numMacroClusters","purity", "SSQ", "cRand"),
    n = 500, assign = "micro", type = "macro")
})
@

The evaluation measures at the macro-cluster level reflect the findings from the
visual analysis of the clustering with D-Stream producing the best results.



\section{Experimental Comparison using an Evolving Data Stream}
\label{examples:full_evolving}

In this section we compare different clustering algorithms on
an evolving data stream. We use
\code{DSD_Benchmark(1)} which creates two
clusters moving in two-dimensional space. One moves from top left to bottom right and the other one moves
from bottom left to top right. Both clusters overlap when they meet exactly in
the center of the data space.

<<>>=
set.seed(0)
stream <- DSD_Memory(DSD_Benchmark(1), 5000)
@



<<moa1, fig=TRUE, include=FALSE, echo=FALSE>>=
plot(stream, 250, xlim = c(0,1), ylim = c(0,1))
arrows(.15,.85,.85,.15, col = rgb(.8,.8,.8,.6), lwd = 10)
arrows(.15,.15,.85,.85, col = rgb(.8,.8,.8,.6), lwd = 10)
reset_stream(stream)
@

\begin{figure}
\centering
\includegraphics[width=.5\linewidth]{streamMOA-moa1}

\caption{Data points from \code{DSD\_Benchmark(1)} at the beginning of the stream.
The two arrows are added to highlight the direction of movement.}
\label{figure:dsd_bench}
\end{figure}


Figure~\ref{figure:dsd_bench} illustrates the structure of the data stream.
Next, we define the clustering algorithms.

<<>>=
algorithms <- list(
  'Sample' = DSC_TwoStage(micro=DSC_Sample(k=100, biased=TRUE),
    macro=DSC_Kmeans(k=2)),
  'Window' = DSC_TwoStage(micro=DSC_Window(horizon=100, lambda=.01),
    macro=DSC_Kmeans(k=2)),

  'D-Stream' = DSC_DStream(gridsize=.1, lambda=.01),
  'DBSTREAM' = DSC_DBSTREAM(r=.05, lambda=.01),
  'DenStream' = DSC_DenStream_MOA(epsilon=.1, lambda=.01),
  'CluStream' = DSC_CluStream_MOA(m=100, k=2)
)
@

We perform the evaluation using \code{evaluate_cluster} which
performs clustering and evaluates
clustering quality
every \code{horizon=250} data points. For sampling and window
we have to specify a macro-clustering algorithm. We use $k$-means
with the true number of clusters $k=2$.

<<>>=
n <- 5000
horizon <- 250
reset_stream(stream)

evaluation <- lapply(algorithms, FUN=function(a) {
  reset_stream(stream)
  evaluate_cluster(a, stream,
    type="macro", assign="micro",
    measure=c("numMicro","numMacro","SSQ", "cRand"),
    n=n, horizon=horizon)
})
@

%#animate_cluster(sample, dsd, macro=DSC_Kmeans(k=2), n=5000, horizon=500, type="both", evaluationMeasure="crand", evaluationAssignmentMethod="model", evaluationType="macro", assign=T, ylim=c(0,1))

First, we look at the development of the corrected Rand index over time.

<<dynamic, fig=TRUE, include=FALSE, width=10, height=4>>=
Position <- evaluation[[1]][,"points"]
cRand <- sapply(evaluation, FUN=function(x) x[,"cRand"])
cRand
matplot(Position, cRand, type="l", lwd=2)
legend("bottomleft", legend=names(evaluation),
  col=1:6, lty=1:6, bty="n", lwd=2)
@
% #barplot(colMeans(cRand), las=2)
<<dynamic_box, fig=TRUE, include=FALSE, width=4, height=4.1>>=
boxplot(cRand, las=2, cex.axis=.8)
@

And then we compare the sum of squares.

<<dynamic2, fig=TRUE, include=FALSE, width=10, height=4>>=
SSQ <- sapply(evaluation, FUN=function(x) x[,"SSQ"])
SSQ
matplot(Position, SSQ, type="l", lwd=2)
legend("topright", legend=names(evaluation),
  col=1:6, lty=1:6, bty="n", lwd=2)
@
% #barplot(colMeans(cRand), las=2)
<<dynamic_box2, fig=TRUE, include=FALSE, width=4, height=4.1>>=
boxplot(SSQ, las=2, cex.axis=.8)
@


\begin{figure}
\centering
\begin{minipage}{.7\linewidth} \centering
\includegraphics[width=1\linewidth]{streamMOA-dynamic}
\end{minipage}
\begin{minipage}{.27\linewidth} \centering
\includegraphics[width=1\linewidth]{streamMOA-dynamic_box}
\end{minipage}

\begin{minipage}{.7\linewidth} \centering
\includegraphics[width=1\linewidth]{streamMOA-dynamic2}
\end{minipage}
\begin{minipage}{.27\linewidth} \centering
\includegraphics[width=1\linewidth]{streamMOA-dynamic_box2}
\end{minipage}

\caption{Evaluation of data stream clustering of an evolving stream.}
\label{figure:data_bng}
\end{figure}

Figure~\ref{figure:data_bng} shows how the different clustering algorithms compare
in terms of the corrected Rand index and the sum of squares. For all
algorithms the performance degrades around position 3000 since both clusters
overlap completely at that point in the stream. The box-plots to the right
indicate that D-Stream and DBSTREAM perform overall better than the other algorithms.

\input{foot.tex}
