Description Usage Arguments Value Examples
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.
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data |
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minSupport |
minimum support for FPM |
mlen |
maximum length of frequent itemsets |
preferredColumn |
column name that is preferred |
preference |
numeric value that multiplies the score |
noCores |
number of cores for parallel computation |
model output (list) with all results including outlier scores
1 2 3 4 | 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)
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