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#' WFPI - Weighted Frequent Pattern Isolation algorithm
#'
#' Algorithm proposed by:
#' J. Kuchar, V. Svatek: Spotlighting Anomalies using Frequent Patterns, Proceedings of the KDD 2017 Workshop on Anomaly Detection in Finance, Halifax, Nova Scotia, Canada, PMLR, 2017.
#'
#' @param data \code{data.frame} or \code{transactions} from \code{arules} with input data
#' @param minSupport minimum support for FPM
#' @param mlen maximum length of frequent itemsets
#' @param preferredColumn column name that is preferred
#' @param preference numeric value that multiplies the score
#' @param noCores number of cores for parallel computation
#' @return model output (list) with all results including outlier scores
#' @import arules foreach doParallel parallel
#' @export
#' @examples
#' library("fpmoutliers")
#' dataFrame <- read.csv(
#' system.file("extdata", "fp-outlier-customer-data.csv", package = "fpmoutliers"))
#' model <- WFPI(dataFrame, minSupport = 0.001, preferredColumn="Car", preference=10)
WFPI <- function(data, minSupport=0.3, mlen=0, preferredColumn="", preference=1.0, noCores=1){
registerDoParallel(noCores)
if(is(data,"data.frame")){
data <- sapply(data,as.factor)
data <- data.frame(data, check.names=F)
txns <- as(data, "transactions")
} else {
txns <- data
}
if(mlen<=0){
variables <- unname(sapply(txns@itemInfo$labels,function(x) strsplit(x,"=")[[1]][1]))
mlen <- length(unique(variables))
}
fitemsets <- apriori(txns, parameter = list(support=minSupport, maxlen=mlen, target="frequent itemsets"))
fiList <- LIST(items(fitemsets))
qualities <- fitemsets@quality[,"support"]
scores <- c()
tx <- NULL
scores <- foreach(tx = as(txns,"list"), .combine = list, .multicombine = TRUE) %dopar% {
transaction = unlist(tx,"list")
coverage <- c()
support <- c()
for(item in seq(1,length(fiList))){
itemset <- fiList[[item]]
if(all(itemset %in% transaction)){
if(preferredColumn!="" && any(grepl(paste(preferredColumn,"=",sep=""), itemset))) {
support <- c(support, preference*1/(qualities[item]*length(itemset)))
} else {
support <- c(support, 1/(qualities[item]*length(itemset)))
}
coverage <- unique(c(coverage, itemset))
}
}
# penalization for incomplete coverage
if(length(coverage)<length(transaction)){
support <- c(support, rep(length(txns),(length(transaction) - length(coverage)) ))
}
if(length(support)>0){
mean(support)
} else {
length(txns)
}
}
scores <- unlist(scores)
stopImplicitCluster()
output <- list()
output$minSupport <- minSupport
output$maxlen <- mlen
output$model <- fitemsets
output$scores <- scores
output
}
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