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.
1 |
data |
|
minSupport |
minimum support for FPM |
mlen |
maximum length of frequent itemsets |
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 <- FPI(dataFrame, minSupport = 0.001)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.