ELKI stands for Environment for Loping KDD applications Index structures, where KDD stands for Knowledge Discovery in Database. It is an open source software used mainly for data mining, with an emphasis on unsupervised learning. It supports various algorithms for cluster analysis and outlier detection. The following are some outlier algorithms:

• Distance-based outlier detection: This is used to specify two parameters. The object is flagged outlier if its fraction, p, for all the data objects that have a distance above d from c. There are many algorithms, such as DBOutlierDetection, DBOutlierScore, KNNOutlier, KNNWeightOutlier, ParallelKNNOutlier, ParallelKNNWeightOutlier, ReferenceBasedOutlierDetection, and so on.
• LOF family methods: This computes density-based local outlier factors on specific parameters. It includes algorithms such as LOF, ParallelLOF, ALOCI, COF, LDF, LDOF, and so on.
• Angle-based outlier detection: This uses the variance analysis of angles, using mostly high-dimensional datasets. Common algorithms include ABOD, FastABOD, and LBABOD.
• Clustering-based outlier detection: This uses EM clustering; if the object does not belong to a cluster, it is taken as an outlier. This includes algorithms such as EMOutlier and KMeansOutlierDetection.
• Subspace outlier detection: This uses the outlier detection method for axis-parallel subspaces. It has algorithms such as SOD, OutRankS1, OUTRES, AggrawalYuNaive, and AggrawalYuEvolutionary.
• Spatial outlier detection: This has large datasets based on locations which are collected from different sources and the data point that is an extreme relative to neighbors. It has algorithms such as CTLuGLSBackwardSearchAlgorithm, CTLuMeanMultipleAttributes, CTLuMedianAlgorithm, CTLuScatterplotOutlier, and so on.