Description Usage Arguments Value Examples

This function performs suspace outlier detection algorithm The implemented method is based on the work of Krigel, H.P., Kroger, P., Schubert, E., Zimek, A., Outlier detection in axis-parallel subspaces of high dimensional data, 2009.

1 |

`data` |
is the data frame containing the observations. Each row represents an observation and each variable is stored in one column. |

`k.nn` |
specifies the value used for calculating the shared nearest neighbors. Note that k.nn should be greater than k.sel. |

`k.sel` |
specifies the number shared nearest neighbors. It can be interpreted as the number of reference set for constructing the subspace hyperplane. |

`alpha` |
specifies the lower limit for selecting subspace. 0.8 is set as default as suggested in the original paper. |

The function returns a vector containing the SOD outlier scores for each observation

1 2 3 4 5 6 7 8 | ```
library(ggplot2)
res.SOD <- Func.SOD(data = TestData[,1:2], k.nn = 10, k.sel = 5, alpha = 0.8)
data.temp <- TestData[,1:2]
data.temp$Ind <- NA
data.temp[order(res.SOD, decreasing = TRUE)[1:10],"Ind"] <- "Outlier"
data.temp[is.na(data.temp$Ind),"Ind"] <- "Inlier"
data.temp$Ind <- factor(data.temp$Ind)
ggplot(data = data.temp) + geom_point(aes(x = x, y = y, color=Ind, shape=Ind))
``` |

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