COF: Connectivity-based Outlier Factor (COF) algorithm
In DDoutlier: Distance & Density-Based Outlier Detection

Description Usage Arguments Details Value Author(s) References Examples

Function to calculate the connectivity-based outlier factor as an outlier score for observations. Suggested by Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002)

1	COF(dataset, k = 5)

`dataset`	The dataset for which observations have a COF score returned
`k`	The number of k-nearest neighbors to construct a SBN-path with, being the number of neighbors for each observation to compare chaining-distance with. k has to be smaller than the number of observations in dataset

COF computes the connectivity-based outlier factor for observations, being the comparison of chaining-distances between observation subject to outlier scoring and neighboring observations. The COF function is useful for outlier detection in clustering and other multidimensional domains.

A vector of COF scores for observations. The greater the COF, the greater outlierness

Jacob H. Madsen

Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002). Enhancing Effectiveness of Outlier Detections for Low Density Patterns. In Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD). Taipei. pp. 535-548. DOI: 10.1007/3-540-47887-6_53

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

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

# 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)

      107        23        42        65        45        36        16        15 
1.9032707 1.7679359 1.7505756 1.5342417 1.4856055 1.4369644 1.4033106 1.3971693 
       88        32       135       118        99       132        34        44 
1.3694610 1.3543871 1.3382724 1.3369073 1.3368508 1.3345104 1.3194247 1.3185439 
       12        25        69        21       120        60        37         7 
1.3166301 1.2983290 1.2917991 1.2809970 1.2791897 1.2760174 1.2753276 1.2671240 
       24        94        33        58        86        57        19       101 
1.2373945 1.2229869 1.2213936 1.2137401 1.2029034 1.1933475 1.1928910 1.1899048 
       63        72       130       110       115        78         9        56 
1.1889238 1.1876947 1.1867315 1.1684751 1.1640476 1.1632635 1.1567338 1.1558400 
       61        17       109       136       134         6        27        67 
1.1470166 1.1454335 1.1450388 1.1440485 1.1352748 1.1239692 1.1184118 1.1166019 
       91        73        11        39        22       131       114        80 
1.1155910 1.1084009 1.1037625 1.0948350 1.0928137 1.0920151 1.0893427 1.0889261 
      116        47       137       111         3        79       108        14 
1.0828155 1.0814884 1.0789796 1.0773016 1.0704047 1.0671822 1.0671178 1.0616814 
       62        20       112       113       148        48       103       149 
1.0612918 1.0536648 1.0499772 1.0489315 1.0487261 1.0475973 1.0459533 1.0362908 
      119        55        71        53        43        83       106         4 
1.0291461 1.0289691 1.0249188 1.0211934 1.0191291 1.0161334 1.0115316 1.0112518 
       74        85        52       133        54        68        51       146 
1.0107934 1.0098611 1.0096940 1.0091748 1.0081562 1.0076383 1.0065801 1.0049652 
       90       126       145       147        84        77        50        98 
1.0036980 1.0007133 1.0002103 1.0000670 0.9994414 0.9984011 0.9979847 0.9978489 
      144       125        38        93        64       122        87       105 
0.9977548 0.9954753 0.9928152 0.9920353 0.9906144 0.9884301 0.9879680 0.9848838 
      142        46        81        13       123         2         5       124 
0.9842588 0.9823830 0.9803341 0.9770811 0.9767662 0.9734547 0.9685606 0.9682788 
       95       127       140       141        49        66        92        59 
0.9616037 0.9603973 0.9587731 0.9534695 0.9533724 0.9522957 0.9516702 0.9490012 
       26        41       128       138         8        82        10        76 
0.9474871 0.9474792 0.9473734 0.9461497 0.9448709 0.9428516 0.9390493 0.9337580 
       40        35       117       121       129        75       102       143 
0.9333097 0.9302308 0.9288584 0.9244572 0.9217561 0.9210744 0.9199636 0.9199636 
       96       104        18        31        30         1        89        70 
0.9189363 0.9173045 0.9172902 0.9170788 0.9144431 0.9114170 0.9092849 0.9069919 
       28        97        29       139       150       100 
0.8938966 0.8878631 0.8829402 0.8824604 0.8524619 0.8322319