do_knno: kNN for outlier detection

In adamethods: Archetypoid Algorithms and Anomaly Detection

Description

Ramaswamy et al. proposed the k-nearest neighbors outlier detection method (kNNo). Each point's anomaly score is the distance to its kth nearest neighbor in the data set. Then, all points are ranked based on this distance. The higher an example's score is, the more anomalous it is.

Usage

do_knno(data, k, top_n)

Arguments

data	Data observations.
k	Number of neighbors of a point that we are interested in.
top_n	Total number of outliers we are interested in.

Value

Vector of outliers.

Author(s)

Guillermo Vinue

References

Ramaswamy, S., Rastogi, R. and Shim, K. Efficient Algorithms for Mining Outliers from Large Data Sets. SIGMOD'00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000, 427-438.

Examples

data(mtcars)
data <- as.matrix(mtcars)
outl <- do_knno(data, 3, 2)
outl
data[outl,]
                 

Example output

Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl.init' failed, running with 'rgl.useNULL = TRUE'. 
[1] 31 29
                mpg cyl disp  hp drat   wt qsec vs am gear carb
Maserati Bora  15.0   8  301 335 3.54 3.57 14.6  0  1    5    8
Ford Pantera L 15.8   8  351 264 4.22 3.17 14.5  0  1    5    4

adamethods documentation built on Aug. 4, 2020, 5:08 p.m.