# ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"

arXiv: https://arxiv.org/abs/1902.03616 · HTML: https://mlsanity.com/paper/1902.03616/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2019.

## Built on by (most cited first, 10 of 10)

- [Unsupervised anomaly detection algorithms on real-world data: how many do we need?](https://mlsanity.com/paper/2305.00735/) (2023)
- [A Triangle Inequality for Cosine Similarity](https://mlsanity.com/paper/2107.04071/) (2021)
- [Accelerating Spherical k-Means](https://mlsanity.com/paper/2107.04074/) (2021)
- [AI-Generated Compromises for Coalition Formation](https://mlsanity.com/paper/2506.06837/) (2025)
- [Natural Language-Based Synthetic Data Generation for Cluster Analysis](https://mlsanity.com/paper/2303.14301/) (2023)
- [Outlier Detection with Cluster Catch Digraphs](https://mlsanity.com/paper/2409.11596/) (2024)
- [Data Aggregation for Hierarchical Clustering](https://mlsanity.com/paper/2309.02552/) (2023)
- [Sparse Partitioning Around Medoids](https://mlsanity.com/paper/2309.02557/) (2023)
- [Outlyingness Scores with Cluster Catch Digraphs](https://mlsanity.com/paper/2501.05530/) (2025)
- [AI-Generated Compromises for Coalition Formation: Modeling, Simulation, and a Textual Case Study](https://mlsanity.com/paper/2512.05983/) (2025)

## Abstract (arXiv)

This paper documents the release of the ELKI data mining framework, version 0.7.5. 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. We will first outline the motivation for this release, the plans for the future, and then give a brief overview over the new functionality in this version. We also include an appendix presenting an overview on the overall implemented functionality.