Description Usage Arguments Value Examples
Algorithm proposed by: X. Tang, G. Li and G. Chen, "Fast Detecting Outliers over Online Data Streams," 2009 International Conference on Information Engineering and Computer Science, Wuhan, 2009, pp. 1-4.
1 |
data |
|
minSupport |
minimum support for FPM |
mlen |
maximum length of frequent itemsets |
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 <- FPCOF(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].
Warning message:
Column(s) 1, 2, 3 not logical or factor. Applying default discretization (see '? discretizeDF').
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