FPOFcontradictness

dataFrame

anIndex

minSupport

<p>maximum length of frequent itemsets</p>

mlen

Algorithms for detection of outliers based on frequent pattern
mining. Such algorithms follow the paradigm: if an instance contains more frequent patterns,
it means that this data instance is unlikely to be an anomaly (He Zengyou, Xu Xiaofei, Huang
Zhexue Joshua, Deng Shengchun (2005) <doi:10.2298/CSIS0501103H>).
The package implements a list of existing state of the art algorithms as well as other published
approaches: FPI, WFPI, FPOF, FPCOF, LFPOF, MFPOF, WCFPOF and WFPOF.

Jaroslav Kuchar

fpmoutliers

Frequent Pattern Mining Outliers

FPOFcontradictness function

Frequent Pattern Outlier Factor — FPOFcontradictness

Frequent Pattern Outlier Factor

FPOFcontradictness: Frequent Pattern Outlier Factor

Description

Usage

Arguments

Value