|Developer(s)||Ludwig Maximilian University of Munich|
|Stable release||0.5.5 / December 11, 2012|
|Preview release||0.6.0~beta2 / October 29, 2013|
|Operating system||Microsoft Windows, Linux, Mac OS|
|License||AGPL (since version 0.4.0)|
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.
The ELKI framework is written in Java and built around a modular architecture. Most currently included algorithms belong to clustering, outlier detection 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 of the doctoral dissertation of Dr. Arthur Zimek, which was awarded "SIGKDD Doctoral Dissertation Award 2009 Runner-up" 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.
Version 0.4 presented at the "Symposium on Spatial and Temporal Databases" 2011 with included various methods for spatial outlier detection won the conferences "best demonstration paper award".
Select included algorithms:
- Cluster analysis:
- K-means clustering
- Expectation-maximization algorithm
- Hierarchical clustering
- Single-linkage clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- OPTICS (Ordering Points To Identify the Clustering Structure), including the extensions OPTICS-OF, DeLi-Clu, HiSC, HiCO and DiSH
- SUBCLU (Density-Connected Subspace Clustering for High-Dimensional Data)
- Canopy clustering algorithm
- Anomaly detection:
- Spatial index structures:
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.
Version 0.4 (September 2011) added algorithms for geo data mining and support for multi-relational database and index structures.
- Weka a similar project by the University of Waikato, with a focus on classification algorithms.
- RapidMiner an application available both as open source as well as commercially with a focus on machine learning.
- Konstanz Information Miner (KNIME) - open source data analytics platform integrated in Eclipse.
- Official web page of ELKI with download and documentation.
- Hans-Peter Kriegel, Peer Kröger, Arthur Zimek (2009). "Outlier Detection Techniques (Tutorial)". 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009) (Bangkok, Thailand). Retrieved 2010-03-26.
- Zimek, A. (2009). "Correlation clustering". ACM SIGKDD Explorations Newsletter 11 (1): 53–54. doi:10.1145/1656274.1656286.
- Zimek, Arthur (2008-06-30), Correlation Clustering, Munich, Germany: Ludwig Maximilian University of Munich, urn:nbn:de:bvb:19-87361
- "SIGKDD Doctoral Disseration Award". ACM SIGKDD. Retrieved 30 May 2010.
- Elke Achtert, Achmed Hettab, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek (2011). "Spatial Outlier Detection: Data, Algorithms, Visualizations". 12th International Symposium on Spatial and Temporal Databases (SSTD 2011) (Minneapolis, MN: Spinger). doi:10.1007/978-3-642-22922-0_41.
- excerpt from "Data Mining Algorithms in ELKI 0.4". Retrieved August 17, 2011.
- Elke Achtert, Hans-Peter Kriegel, Arthur Zimek (2008). "ELKI: A Software System for Evaluation of Subspace Clustering Algorithms". Proceedings of the 20th international conference on Scientific and Statistical Database Management (SSDBM 08) (Hong Kong, China: Springer). doi:10.1007/978-3-540-69497-7_41.
- Elke Achtert, Thomas Bernecker, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek (2009). "ELKI in time: ELKI 0.2 for the performance evaluation of distance measures for time series". Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases (SSTD 2010) (Aalborg, Dänemark: Springer). doi:10.1007/978-3-642-02982-0_35.
- Elke Achtert, Hans-Peter Kriegel, Lisa Reichert, Erich Schubert, Remigius Wojdanowski, Arthur Zimek (2010). "Visual Evaluation of Outlier Detection Models". 15th International Conference on Database Systems for Advanced Applications (DASFAA 2010) (Tsukuba, Japan: Spinger). doi:10.1007/978-3-642-12098-5_34.
- Elke Achtert, Sascha Goldhofer, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek (2012). "Evaluation of Clusterings Metrics and Visual Support". 28th International Conference on Data Engineering (ICDE) (Washington, DC). doi:10.1109/ICDE.2012.128.
- Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek (2013). "Interactive Data Mining with 3D-Parallel-Coordinate-Trees". Proceedings of the ACM International Conference on Management of Data (SIGMOD) (New York City, NY).