ELKI: Difference between revisions
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Revision as of 18:39, 2 May 2010
| Developer(s) | Ludwig Maximilian University of Munich |
|---|---|
| Stable release | 0.3.0
/ March 30, 2010 |
| Repository | |
| Operating system | Cross-platform |
| Type | Data Mining |
| License | unknown |
| Website | http://www.dbs.ifi.lmu.de/research/KDD/ELKI |
Environment for DeveLoping KDD-Applications Supported by Index-Structures (ELKI) is a data mining software developed for use in research and teaching 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.
Description
The ELKI framework is written in Java and built around a modular architecture. Most 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 extensability, readability and reusability in mind, but it is not optimized for performance. It currently does not offer intergration with business intelligence applications or even an interface to common database management systems via SQL (albeit this could be added). The application of the algorithm requires knowledge about their use and study of documentation. The audience are students, researchers and software engineers
Included algorithms
Some of the included algorithms:
- K-means clustering
- Expectation-maximization algorithm
- Apriori algorithm
- Single-linkage clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- OPTICS (Ordering Points To Identify the Clustering Structure)
- LOF (Local outlier factor)
- SUBCLU (Density-Connected Subspace Clustering for High-Dimensional Data)
- R*-tree
Licensing
The website or source code does not give an explicit license, it should therefore be considered copyrighted. The authors have stated that research use is acceptable but attribution is required. For commercial use, an explicit license is required.
Version history
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 on subspace clustering algorithms.[2]
Version 0.2 (July 2009) added functionality for time series analysis, in particular distance functions for this. [3]
Version 0.3 (March 2010) extended the choice of anomaly detection algorithms and visualization modules [4]
Related applications
- 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.
External links
References
- ^ Hans-Peter Kriegel, Peer Kröger, Arthur Zimek (2009). "Outlier Detection Techniques (Tutorial)" (PDF). 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009). Bangkok, Thailand. Retrieved 2010-03-26.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ^ Elke Achtert, Hans-Peter Kriegel, Arthur Zimek (2008). "ELKI: A Software System for Evaluation of Subspace Clustering Algorithms" (PDF). 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.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ^ 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" (PDF). 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.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ^ 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.
{{cite journal}}: CS1 maint: multiple names: authors list (link)