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

Erich Schubert, Arthur Zimek

Research output: Contribution to journalJournal articleResearch

Abstract

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.
Original languageEnglish
Article numberabs/1902.03616
Journalarxiv.org
Number of pages134
Publication statusPublished - 10 Feb 2019

Fingerprint

Data mining
Cluster analysis
Benchmarking
Scalability
Students

Keywords

  • cs.LG
  • stat.ML

Cite this

@article{8691020f6e32441cac18b555d1d3e829,
title = "ELKI: A large open-source library for data analysis: ELKI Release 0.7.5 {"}Heidelberg{"}",
abstract = "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.",
keywords = "cs.LG, stat.ML",
author = "Erich Schubert and Arthur Zimek",
year = "2019",
month = "2",
day = "10",
language = "English",
journal = "arxiv.org",

}

ELKI: A large open-source library for data analysis : ELKI Release 0.7.5 "Heidelberg". / Schubert, Erich; Zimek, Arthur.

In: arxiv.org, 10.02.2019.

Research output: Contribution to journalJournal articleResearch

TY - JOUR

T1 - ELKI: A large open-source library for data analysis

T2 - ELKI Release 0.7.5 "Heidelberg"

AU - Schubert, Erich

AU - Zimek, Arthur

PY - 2019/2/10

Y1 - 2019/2/10

N2 - 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.

AB - 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.

KW - cs.LG

KW - stat.ML

M3 - Journal article

JO - arxiv.org

JF - arxiv.org

M1 - abs/1902.03616

ER -