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Algorithm proposed by: He, Z., Xu, X., Huang, J. Z., Deng, S.: FP-Outlier: Frequent Pattern Based Outlier Detection. Computer Science and Information Systems, Vol. 2, No. 1, 103-118. (2005)
FPOF(data, minSupport = 0.3, mlen = 0, noCores = 1)
data.frame or transactions from arules with input data
data.frame
transactions
arules
minimum support for FPM
maximum length of frequent itemsets
number of cores for parallel computation
model output (list) with all results including outlier scores
# NOT RUN { library("fpmoutliers") dataFrame <- read.csv( system.file("extdata", "fp-outlier-customer-data.csv", package = "fpmoutliers")) model <- FPOF(dataFrame, minSupport = 0.001) # }
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