ELKI: Environment for Developing KDD-Applications Supported by Index-Structures. ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance and scalability, ELKI offers data index structures such as the R*-tree that can provide major performance gains. ELKI is designed to be easy to extend for researchers and students in this domain, and welcomes contributions of additional methods. ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms.

References in zbMATH (referenced in 13 articles , 1 standard article )

Showing results 1 to 13 of 13.
Sorted by year (citations)

  1. Filzmoser, Peter; Gregorich, Mariella: Multivariate outlier detection in applied data analysis: global, local, compositional and cellwise outliers (2020)
  2. Kandanaarachchi, Sevvandi; Muñoz, Mario A.; Hyndman, Rob J.; Smith-Miles, Kate: On normalization and algorithm selection for unsupervised outlier detection (2020)
  3. Eiras-Franco, Carlos; Martínez-Rego, David; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio: Large scale anomaly detection in mixed numerical and categorical input spaces (2019)
  4. Erich Schubert, Arthur Zimek: ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 ”Heidelberg” (2019) arXiv
  5. Michael Hahsler; Matthew Piekenbrock; Derek Doran: dbscan: Fast Density-Based Clustering with R (2019) not zbMATH
  6. Liu, Siyuan; Qu, Qiang; Wang, Shuhui: Heterogeneous anomaly detection in social diffusion with discriminative feature discovery (2018)
  7. Ha, Jihyun; Seok, Seulgi; Lee, Jong-Seok: A precise ranking method for outlier detection (2015)
  8. Schubert, Erich; Zimek, Arthur; Kriegel, Hans-Peter: Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection (2014)
  9. Telang, Aditya; Deepak, P.; Joshi, Salil; Deshpande, Prasad; Rajendran, Ranjana: Detecting localized homogeneous anomalies over spatio-temporal data (2014) ioport
  10. Wells, Jonathan R.; Ting, Kai Ming; Washio, Takashi: LiNearN: a new approach to nearest neighbour density estimator (2014)
  11. Zimek, Arthur; Schubert, Erich; Kriegel, Hans-peter: A survey on unsupervised outlier detection in high-dimensional numerical data (2012)
  12. Achtert, Elke; Hettab, Ahmed; Kriegel, Hans-Peter; Schubert, Erich; Zimek, Arthur: Spatial outlier detection: Data, algorithms, visualizations (2011) ioport
  13. Achtert, Elke; Bernecker, Thomas; Kriegel, Hans-Peter; Schubert, Erich; Zimek, Arthur: ELKI in time: ELKI 0.2 for the performance evaluation of distance measures for time series (2009) ioport

Further publications can be found at: https://elki-project.github.io/publications