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

TL;DR

ELKI is an open-source Java framework for research in unsupervised data analysis, offering scalable, highly parameterizable algorithms and data structures to facilitate benchmarking and algorithm development.

Contribution

This release introduces a comprehensive, extendable data mining library with optimized data structures and a focus on research and benchmarking in clustering and outlier detection.

Findings

Provides a large collection of parameterizable algorithms

Includes efficient data index structures like R*-tree

Facilitates research and benchmarking in data analysis

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.