gbod: Grid Based Outlier Detection
In p-jayanna/gbod: Outlier Detection

Description Usage Arguments Author(s) References Examples

View source: R/gbod.R

Grid Based Outlier Detection in Large Data Sets.

1	gbod(df, n, outlierpercent)

`df`	gata dataframe with ertrag and durchsatz columns , grid scale, and outlier percentage
`n`	Grid scale
`outlierpercent`	outlier percentage

Ying Gu, Praveenkumar Jayanna

Grid Based Outlier Detection in Large Data Sets Ying Gu <e2><88><97> , Ram Kumar Ganesan <e2><88><97> , Benjamin Bischke <e2><88><97> , Alexander Maier <e2><80><a0> , Thilo Steckel <e2><80><a1> , Heinrich Warkentin <e2><80><a1> , Ansgar Bernardi <e2><88><97> and Andreas Dengel <e2><88><97> <e2><88><97> German Research Center for Artificial Intelligence <e2><80><a0> Fraunhofer-Application Center Industrial Automation (IOSB-INA) <e2><80><a1> CLAAS E-Systems KGaA mbH & Co KG

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

df_csv <- read.csv('/home/praveen/AGATA/gbod.csv', stringsAsFactors = F)
finaldataset<-gbod(df_csv,10,5)
outlierplot(finaldataset)




{ ~kwd1 }
{ ~kwd2 }