FPOF: FPOF - Frequent Pattern Outlier Factor algorithm

In fpmoutliers: Frequent Pattern Mining Outliers

Description

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)

Usage

FPOF(data, minSupport = 0.3, mlen = 0, noCores = 1)

Arguments

data	data.frame or transactions from arules with input data
minSupport	minimum support for FPM
mlen	maximum length of frequent itemsets
noCores	number of cores for parallel computation

Value

model output (list) with all results including outlier scores

Examples

library("fpmoutliers")
dataFrame <- read.csv(
     system.file("extdata", "fp-outlier-customer-data.csv", package = "fpmoutliers"))
model <- FPOF(dataFrame, minSupport = 0.001)

Example output

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'). 

fpmoutliers documentation built on May 2, 2019, 8:53 a.m.