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#'@title The ProClus Algorithm for Projected Clustering
#'
#'@description The ProClus algorithm works in a manner similar to K-Medoids.
#' Initially, a set of medoids of a size that is proportional to k is chosen.
#' Then medoids that are likely to be outliers or are part of a cluster that is
#' better represented by another medoid are removed until k medoids are left.
#' Clusters are then assumed to be around these medoids.
#'
#'@param data A Matrix of input data.
#'@param k Number of Clusters to be found.
#'@param d Average number of dimensions in which the clusters reside
#'@references C. C. Aggarwal and C. Procopiuc \emph{Fast Algorithms for
#' Projected Clustering}. In Proc. ACM SIGMOD 1999.
#'
#'
#'@examples
#'data("subspace_dataset")
#'ProClus(subspace_dataset,k=12,d=2.5)
#'@family subspace clustering algorithms
#'@export
ProClus <- function(data,k=4,d=3) {
arr <- java_object_from_data(data)
#Now that the data is in the correct format, we can call into our Java Code that will then call into the
#actual implementation of the Algorithm
res <- rJava::.jcall("ClusteringApplier",returnSig="[Li9/subspace/base/Cluster;",method="proclus",arr,
as.integer(k),
as.integer(d),
evalArray=F)
#We can then turn the Java Clustering Object that was returned into an R-Friendly S3-Object
res <- r_clusters_from_java_clusters(res)
return(res)
}
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