LOF
Synopsis
A filter that applies the LOF (Local Outlier Factor) algorithm to compute an "outlier" score for each instance in the data. Can use multiple cores/cpus to speed up the LOF computation for large datasets. Nearest neighbor search methods and distance functions are pluggable.
Available via the package management system for Weka >= 3.7.2 (localOutlierFactor)
For more information, see:
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record. 29(2):93-104.
Options
The table below describes the options available for LOF.
Option |
Description |
|---|---|
NNSearch |
The nearest neighbour search algorithm to use (Default: weka.core.neighboursearch.LinearNNSearch). |
minPointsLowerBound |
The lower bound (minPtsLB) to use on the range for k when determining the maximum LOF value |
minPointsUpperBound |
The upper bound (minPtsUB) to use on the range for k when determining the maximum LOF value |
numExecutionSlots |
The number of execution slots (threads) to use for finding LOF values. |
Capabilities
The table below describes the capabilities of LOF.
Capability |
Supported |
|---|---|
Class |
Binary class, No class, Nominal class, Missing class values, Numeric class, Date class |
Attributes |
Numeric attributes, Binary attributes, Nominal attributes, Empty nominal attributes, Missing values, Unary attributes, Date attributes |
Min # of instances |
0 |