# filters.lof¶

The **Local Outlier Factor (LOF) filter** was introduced as a method
of determining the degree to which an object is an outlier. This filter
is an implementation of the method
described in [Breunig2000].

The filter creates three new dimensions, `KDistance`

,
`LocalReachabilityDistance`

and `LocalOutlierFactor`

, all of which are
double-precision floating values. The `KDistance`

dimension records the
Euclidean distance between a point and it’s k-th nearest neighbor (the number
of k neighbors is set with the minpts option). The
`LocalReachabilityDistance`

is the inverse of the mean
of all reachability distances for a neighborhood of points. This reachability
distance is defined as the max of the Euclidean distance to a neighboring point
and that neighbor’s own previously computed `KDistance`

. Finally, each point
has a `LocalOutlierFactor`

which is the mean of all
`LocalReachabilityDistance`

values for the neighborhood. In each case, the
neighborhood is the set of k nearest neighbors.

In practice, setting the minpts parameter appropriately and subsequently
filtering outliers based on the computed `LocalOutlierFactor`

can be
difficult. The authors present some work on establishing upper and lower bounds
on LOF values, and provide some guidelines on selecting minpts values, which
users of this filter should find instructive.

Note

To inspect the newly created, non-standard dimensions, be sure to write to an output format that can support arbitrary dimensions, such as BPF.

## Example¶

The sample pipeline below uses computes the LOF with a neighborhood of 20
neighbors, followed by a range filter to crop out points whose
`LocalOutlierFactor`

exceeds 1.2 before writing the output.

```
[
"input.las",
{
"type":"filters.lof",
"minpts":20
},
{
"type":"filters.range",
"limits":"LocalOutlierFactor[:1.2]"
},
"output.laz"
]
```

## Options¶

- minpts
- The number of k nearest neighbors. [Default: 10]

[Breunig2000] | Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J., 2000. LOF: Identifying Density-Based Local Outliers. Proc. 2000 Acm Sigmod Int. Conf. Manag. Data 1–12. |