Screenshot of ELKI 0.4 visualizing OPTICS cluster analysis. |
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| Developer(s) | Ludwig Maximilian University of Munich |
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| Stable release | 0.4.0 / September 20, 2011 |
| Written in | Java |
| Operating system | Microsoft Windows, Linux, Mac OS |
| Platform | Java platform |
| Type | Data mining |
| License | AGPL (since version 0.4.0) |
| Website | http://elki.dbs.ifi.lmu.de/ |
ELKI (for Environment for DeveLoping KDD-Applications Supported by Index-Structures) is a knowledge discovery in databases (KDD, "data mining") software framework developed for use in research and teaching by the database systems research unit of Professor Hans-Peter Kriegel at the Ludwig Maximilian University of Munich, Germany. It aims at allowing the development and evaluation of advanced data mining algorithms and their interaction with database index structures.
Contents |
The ELKI framework is written in Java and built around a modular architecture. Most currently included algorithms belong to clustering, outlier detection[1] and database indexes. A key concept of ELKI is to allow the combination of arbitrary algorithms, data types, distance functions and indexes and evaluate these combinations. When developing new algorithms or index structures, the existing components can be reused and combined.
The university project is developed for use in teaching and research. The source code is written with extensibility, readability and reusability in mind, but it is not extensively optimized for performance. A scientific evaluation comparing run times thus is only sound when both algorithms are implemented within ELKI so they share the same cost. It currently does not offer integration with business intelligence applications or even an interface to common database management systems via SQL. The application of the algorithms requires knowledge about their use and study of documentation. The audience are students, researchers and software engineers.
The visualization modules use SVG for scalable graphics output, and Apache Batik for rendering of the user interface as well as lossless export into PostScript and PDF for easy inclusion in scientific publications in LaTeX.
ELKI started as implementation[2] of the doctoral dissertation of Dr. Arthur Zimek,[3] which was awarded "SIGKDD Doctoral Dissertation Award 2009 Runner-up"[4] by the Association for Computing Machinery for its contributions to correlation clustering. The algorithms published as part of the dissertation (4C, COPAC, HiCO, ERiC, CASH) are available in ELKI.[2]
Version 0.4 presented at the "Symposium on Spatial and Temporal Databases" 2011 with included various methods for spatial outlier detection[5] won the conferences "best demonstration paper award".
Select included algorithms[6]:
Version 0.1 (July 2008) contained several Algorithms from cluster analysis and anomaly detection, as well as some index structures such as the R*-tree. The focus of the first release was on subspace clustering and correlation clustering algorithms.[7]
Version 0.2 (July 2009) added functionality for time series analysis, in particular distance functions for time series.[8]
Version 0.3 (March 2010) extended the choice of anomaly detection algorithms and visualization modules.[9]
Version 0.4 (September 2011) added algorithms for geo data mining and support for multi-relational database and index structures.[5]