davnn / little-data

Supplementary material for "Little data is often enough for distance-based outlier detection"

Supplementary material

@article{muhrLittleDataOften2022,
  title = {Little Data Is Often Enough for Distance-Based Outlier Detection},
  author = {Muhr, David and Affenzeller, Michael},
  year = {2022},
  month = jan,
  journal = {Procedia Computer Science},
  series = {3rd {{International Conference}} on {{Industry}} 4.0 and {{Smart Manufacturing}}},
  volume = {200},
  pages = {984--992},
  issn = {1877-0509},
  doi = {10.1016/j.procs.2022.01.297},
  langid = {english},
  keywords = {anomaly detection,clustering,k-means,knn,local outlier factor,lof,nearest neighbors,outlier detection,prototypes,unsupervised}
}