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)
|
Registered S3 method overwritten by 'pryr':
method from
print.bytes Rcpp
Apriori
Parameter specification:
confidence minval smax arem aval originalSupport maxtime support minlen
NA 0.1 1 none FALSE TRUE 5 0.001 1
maxlen target ext
3 frequent itemsets TRUE
Algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE FALSE TRUE 2 TRUE
Absolute minimum support count: 0
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[6 item(s), 10 transaction(s)] done [0.00s].
sorting and recoding items ... [6 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 done [0.00s].
sorting transactions ... done [0.00s].
writing ... [22 set(s)] done [0.00s].
creating S4 object ... done [0.00s].
FI lengths: 0.33668327331543 ( 134268832 )
FI qualities: 0.000627040863037109 ( 134278992 )
FI coverages: 0.0152170658111572 ( 134463736 )
FI multiply: 0.00622749328613281 ( 134529328 )
Penalization: 0.016606330871582 ( 134551352 )
Means: 0.00153565406799316 ( 134553992 )
Warning message:
Column(s) 1, 2, 3 not logical or factor. Applying default discretization (see '? discretizeDF').
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