NAN: Natural Neighbor (NAN) algorithm to return the self-adaptive...
In DDoutlier: Distance & Density-Based Outlier Detection

Description Usage Arguments Details Value Author(s) References Examples

Function to identify natural neighbors and the right k-parameter for kNN graphs as suggested by Zhu, Q., Feng, Ji. & Huang, J. (2016)

1	NAN(dataset, NaN_Edges = FALSE)

`dataset`	The dataset for which natural neighbors are identified along with a k-parameter
`NaN_Edges`	Choice for computing natural neighbors. Computational heavy to compute

NAN computes the natural neighbor eigenvalue and identifies natural neighbors in a dataset. The natural neighbor eigenvalue is powerful as k-parameter for computing a k-nearest neighborhood, being suitable for outlier detection, clustering or predictive modelling. Natural neighbors are defined as two observations being mutual k-nearest neighbors. A kd-tree is used for kNN computation, using the kNN() function from the 'dbscan' package

`NaN_Num`	The number of in-degrees for observations given r
`r`	Natural neighbor eigenvalue. Useful as k-parameter
`NaN_Edges`	Matrix of edges for natural neighbors
`n_NaN`	The number of natural neighbors

Jacob H. Madsen

Zhu, Q., Feng, Ji. & Huang, J. (2016). Natural neighbor: A self-adaptive neighborhood method without parameter K. Pattern Recognition Letters. pp. 30-36. DOI: 10.1016/j.patrec.2016.05.007

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

# Identify the right k-parameter
K <- NAN(X, NaN_Edges=FALSE)$r

# Use the k-setting in an abitrary outlier detection algorithm
outlier_score <- LOF(dataset=X, k=K)

# 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)

[1] "r update: 2"
[1] "r update: 3"
[1] "r update: 4"
[1] "r update: 5"
       42       107        23       110        63        25       135        21 
2.4799601 2.0147424 1.9591426 1.6025838 1.5581248 1.5286564 1.4801222 1.4755714 
      109        32        24        65       132        37        58        44 
1.4551038 1.4106112 1.3764034 1.3743801 1.3667996 1.3654501 1.3593661 1.3505534 
       60        88        69        61        94        99        19        85 
1.3493642 1.3408929 1.3370690 1.3356785 1.3291507 1.3243655 1.3025318 1.3015193 
      101       118        14        16        45        33        36        15 
1.2963231 1.2925090 1.2837610 1.2806953 1.2621335 1.2619762 1.2614239 1.2589051 
      119        38        27       120       149       115         6        47 
1.2505891 1.2482791 1.2467165 1.2390413 1.2386808 1.2214419 1.2166377 1.2026102 
       80         9        39        20        86        51        11       130 
1.1740525 1.1720509 1.1720509 1.1660885 1.1514986 1.1462049 1.1439846 1.1381879 
       22       142        91        17       126        12       137        72 
1.1310676 1.1296311 1.1266345 1.1237830 1.1234314 1.1231084 1.1158796 1.1146508 
       34        67       103        50        49        73        87       131 
1.1123642 1.1104066 1.1094105 1.0933311 1.0896507 1.0835872 1.0793313 1.0688565 
       53       114        57        74       123        48         3        54 
1.0686060 1.0666094 1.0654710 1.0643763 1.0609483 1.0585192 1.0575149 1.0574148 
       82       106        78       122        81       116       133        62 
1.0571588 1.0504145 1.0483315 1.0473884 1.0469181 1.0460889 1.0431377 1.0366131 
        2        70        64       136         7       108        98        92 
1.0322236 1.0311946 1.0300098 1.0290194 1.0280724 1.0249838 1.0247701 1.0213139 
       68        43        35       138       140        76        79       100 
1.0212936 1.0212300 1.0210219 1.0202060 1.0165880 1.0152831 1.0120380 1.0114523 
       40        10         5         1       104        90        97       124 
1.0100374 1.0092746 1.0088640 1.0084973 1.0082068 1.0077902 1.0077677 1.0071882 
      121       105       127        31       102       143       112        71 
1.0069813 1.0055536 1.0040905 1.0039576 1.0038486 1.0038486 1.0035404 1.0019179 
       56        41       129       134       141        75       128       117 
1.0012099 0.9998504 0.9985588 0.9975503 0.9957788 0.9953949 0.9925829 0.9918775 
      145       147       144       146       139        18       111        59 
0.9913695 0.9901533 0.9886461 0.9885251 0.9847184 0.9835520 0.9784784 0.9768842 
       96        77        95       125        46        30        52        26 
0.9755162 0.9749787 0.9737883 0.9702753 0.9686532 0.9674442 0.9640250 0.9636804 
        4       113        89        83       148        13        55       150 
0.9604773 0.9574717 0.9547550 0.9545726 0.9449789 0.9442198 0.9398881 0.9383261 
       28        93        84         8        66        29 
0.9314671 0.9311334 0.9280119 0.9186461 0.9120239 0.8972745