Commit 15f1c36e authored by Matthias Carnein's avatar Matthias Carnein

Documentation improvements

parent cafc3966
......@@ -9,8 +9,9 @@
#'
#' @param k number of macro-clusters
#' @param generations number of EA generations performed during reclustering
#' @param crossoverRate cross-over rate for the evolutionary algorithm
#' @param mutationRate mutation rate for the evolutionary algorithm
#' @param populationsize number of solutions that the evolutionary algorithm maintains
#' @param populationSize number of solutions that the evolutionary algorithm maintains
#'
#' @author Matthias Carnein \email{Matthias.Carnein@@uni-muenster.de}
#'
......
......@@ -8,8 +8,8 @@
#' By iteratively selecting better solutions, an evolutionary pressure is created which improves the clustering over time.
#' Since the evolutionary algorithm is incremental, it is possible to apply it between observations, e.g. in the idle time of the stream.
#' Whenever there is idle time, we can call the \code{recluster} function of the reference class to improve the macro-clusters (see example).
#' Alternatively, the evolutionary algorithm can be applied as a traditional reclustering step, or a combination of both.
#' For simplicity, this implementation also allows to evaluate a fixed number of generations after each observation and during reclustering.
#' The evolutionary algorithm can also be applied as a traditional reclustering step, or a combination of both.
#' In addition, this implementation also allows to evaluate a fixed number of generations after each observation.
#'
#' @param r radius threshold for micro-cluster assignment
#' @param lambda decay rate
......@@ -19,11 +19,13 @@
#' @param reclusterGenerations number of EA generations performed during reclustering
#' @param crossoverRate cross-over rate for the evolutionary algorithm
#' @param mutationRate mutation rate for the evolutionary algorithm
#' @param populationsize number of solutions that the evolutionary algorithm maintains
#' @param populationSize number of solutions that the evolutionary algorithm maintains
#' @param initializeAfter number of micro-cluster required for the initialization of the evolutionary algorithm.
#'
#' @author Matthias Carnein \email{Matthias.Carnein@@uni-muenster.de}
#'
#' @references Carnein M. and Trautmann H. (2018), "evoStream - Evolutionary Stream Clustering Utilizing Idle Times", Big Data Research.
#'
#' @examples
#' stream <- DSD_Memory(DSD_Gaussians(k = 3, d = 2), 1000)
#'
......
......@@ -12,9 +12,11 @@ DSC_EA(k, generations = 2000, crossoverRate = 0.8,
\item{generations}{number of EA generations performed during reclustering}
\item{crossoverRate}{cross-over rate for the evolutionary algorithm}
\item{mutationRate}{mutation rate for the evolutionary algorithm}
\item{populationsize}{number of solutions that the evolutionary algorithm maintains}
\item{populationSize}{number of solutions that the evolutionary algorithm maintains}
}
\description{
Reclustering using an evolutionary algorithm.
......
......@@ -26,9 +26,9 @@ DSC_evoStream(r, lambda = 0.001, tgap = 100, k = 2,
\item{mutationRate}{mutation rate for the evolutionary algorithm}
\item{initializeAfter}{number of micro-cluster required for the initialization of the evolutionary algorithm.}
\item{populationSize}{number of solutions that the evolutionary algorithm maintains}
\item{populationsize}{number of solutions that the evolutionary algorithm maintains}
\item{initializeAfter}{number of micro-cluster required for the initialization of the evolutionary algorithm.}
}
\description{
Stream clustering algorithm based on evolutionary optimization.
......@@ -38,8 +38,8 @@ Evolutionary algorithms create slight variations by combining and randomly modif
By iteratively selecting better solutions, an evolutionary pressure is created which improves the clustering over time.
Since the evolutionary algorithm is incremental, it is possible to apply it between observations, e.g. in the idle time of the stream.
Whenever there is idle time, we can call the \code{recluster} function of the reference class to improve the macro-clusters (see example).
Alternatively, the evolutionary algorithm can be applied as a traditional reclustering step, or a combination of both.
For simplicity, this implementation also allows to evaluate a fixed number of generations after each observation and during reclustering.
The evolutionary algorithm can also be applied as a traditional reclustering step, or a combination of both.
In addition, this implementation also allows to evaluate a fixed number of generations after each observation.
}
\examples{
stream <- DSD_Memory(DSD_Gaussians(k = 3, d = 2), 1000)
......@@ -67,6 +67,9 @@ evoStream$RObj$recluster(1000)
reset_stream(stream)
plot(evoStream, stream, type="both")
}
\references{
Carnein M. and Trautmann H. (2018), "evoStream - Evolutionary Stream Clustering Utilizing Idle Times", Big Data Research.
}
\author{
Matthias Carnein \email{Matthias.Carnein@uni-muenster.de}
......
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