LOOP: Local Outlier Probability (LOOP) algorithm

In DDoutlier: Distance & Density-Based Outlier Detection

Description

Function to calculate the Local Outlier Probability (LOOP) as an outlier score for observations. Suggested by Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009)

Usage

LOOP(dataset, k = 5, lambda = 3)

Arguments

dataset	The dataset for which observations have a LOOP score returned
k	The number of k-nearest neighbors to compare density with
lambda	Multiplication factor for standard deviation. The greater lambda, the smoother results. Default is 3 as used in original papers experiments

Details

LOOP computes a local density based on probabilistic set distance for observations, with a user-given k-nearest neighbors. The density is compared to the density of the respective nearest neighbors, resulting in the local outlier probability. The values ranges from 0 to 1, with 1 being the greatest outlierness. A kd-tree is used for kNN computation, using the kNN() function from the 'dbscan' package. The LOOP function is useful for outlier detection in clustering and other multidimensional domains

Value

A vector of LOOP scores for observations. 1 indicates outlierness and 0 indicate inlierness

Author(s)

Jacob H. Madsen

References

Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009). LoOP: Local Outlier Probabilities. In ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China. pp. 1649-1652. DOI: 10.1145/1645953.1646195

Examples

# Create dataset
X <- iris[,1:4]

# Find outliers by setting an optional k
outlier_score <- LOOP(dataset=X, k=10, lambda=3)

# Sort and find index for most outlying observations
names(outlier_score) <- 1:nrow(X)
sort(outlier_score, decreasing = TRUE)

# Inspect the distribution of outlier scores
hist(outlier_score)

Example output

         42          23         107          14          16          25 
0.936235418 0.773293482 0.745985621 0.589744874 0.586207057 0.547829870 
         99          44         115         109         135          63 
0.473909911 0.460917905 0.447284155 0.395413064 0.395281509 0.390668087 
         45         132          60          15         119          61 
0.388528610 0.386350112 0.381010565 0.380288212 0.375649386 0.374856638 
         80          24          33          37          36          85 
0.359418608 0.352309313 0.349816427 0.347512860 0.347218415 0.344395886 
         21         118         101          69          58          65 
0.340537014 0.335931452 0.334369942 0.330427980 0.319320580 0.307373705 
         26         110          94           7          19         120 
0.301971937 0.287307483 0.281688962 0.277729412 0.269540000 0.269130748 
        130           9          38          88          86          51 
0.268525183 0.259597736 0.256941436 0.254001561 0.245956522 0.239546570 
        149         142          32          91          72          53 
0.237211160 0.232868294 0.207345522 0.202257878 0.193636419 0.163476997 
         20          54          57         126         114          34 
0.140228789 0.139833437 0.135078667 0.126182512 0.125330986 0.123724899 
         74          67          68          39         137         122 
0.122223977 0.120802321 0.117716381 0.109209009 0.106387188 0.101191249 
         78          82         147          43          17         136 
0.096294402 0.095803081 0.090993339 0.090498848 0.087338896 0.087113616 
         71         123          41         134          12          27 
0.086457913 0.083747574 0.070724734 0.070178971 0.066923954 0.064599193 
        111         108          22          89          49          62 
0.062053651 0.061418371 0.059202167 0.059060506 0.054719850 0.049116040 
         77         133         138           6         146         104 
0.048266840 0.044655165 0.032020039 0.031071969 0.022247127 0.018822273 
         47         131          75         116          46          81 
0.018388101 0.016851085 0.012537963 0.010731342 0.008831521 0.007390367 
        145          55          50         105           1           2 
0.006810598 0.002752395 0.001602346 0.001244878 0.000000000 0.000000000 
          3           4           5           8          10          11 
0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 
         13          18          28          29          30          31 
0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 
         35          40          48          52          56          59 
0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 
         64          66          70          73          76          79 
0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 
         83          84          87          90          92          93 
0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 
         95          96          97          98         100         102 
0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 
        103         106         112         113         117         121 
0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 
        124         125         127         128         129         139 
0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 
        140         141         143         144         148         150 
0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 

DDoutlier documentation built on May 1, 2019, 10:20 p.m.