Description Usage Arguments Details Value Author(s) References Examples

A function that finds the local outlier factor (Breunig et al.,2000) of
the matrix "data" using k neighbours. The local outlier factor (LOF) is a measure of outlierness
that is calculated for each observation. The user decides whether or not an observation
will be considered an outlier based on this measure. The LOF takes into consideration
the density of the neighbourhood around the observation to determine its outlierness. This
is a faster implementation of LOF by using a different data structure and distance calculation function compared to `lofactor()`

function available in dprep package. It also supports multiple k values to be calculated in parallel, as well as various distance measures besides the default Euclidean distance.

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`data` |
The data set to be explored, which can be a data.frame or matrix |

`k` |
The kth-distance to be used to calculate LOFs. k can be a vector which contains multiple k values based on which LOFs need to be calculated. |

`cores` |
optional, The number of cores to be used for parallel computing. If not provided, the maximum number of cores available is used by default. |

`...` |
The parameters to be passed to |

The LOFs are calculated over multiple k values in parallel, and the maximum number of the cpus will be utilised to achieve the best performance.

`lof` |
A matrix with the local outlier factor of each observation as rows and each k value as columns |

Yingsong Hu, Wayne Murray and Yin Shan, Australia

Breuning, M., Kriegel, H., Ng, R.T, and Sander. J. (2000). LOF: Identifying density-based local outliers. In Proceedings of the ACM SIGMOD International Conference on Management of Data.

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```
Loading required package: doParallel
Loading required package: foreach
Loading required package: iterators
Loading required package: parallel
```

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