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Elkiku-Eltern Kind Kurse, Dresden, Germany. 360 likes · 16 were here. Babymassage, Trageberatung & Kinder Betreuung im privaten Rahmen bei dir zu Hause.
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Schwimmschule H. P. & B. Rasch Rütigässli 15 A 3800 Matten 033 822 09 22 aqua-sport@rasch.ch internet http://www.rasch.ch ELKI-Kurs Frühling 2019 Nr. Tag Zeit ...
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- Overview
- Quick Summary
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- Background
- The ELKI wiki: Tutorials, HowTos, Documentation
- Getting ELKI: Download and Citation Policy
- Efficiency Benchmarking with ELKI
- Bug Reports and Contact
- Design Goals
- Building ELKI
Environment for Developing KDD-Applications Supported by Index-Structures
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 many data index structures such as the R*-tree that can provide major performance gai...
You can download precompiled ELKI releases from the home page, or you can use standard Java dependency management such as Gradle and Maven.
Gradle:
Data mining research leads to many algorithms for similar tasks. A fair and useful comparison of these algorithms is difficult due to several reasons:
•Implementations of comparison partners are not at hand.
•If implementations of different authors are provided, an evaluation in terms of efficiency is biased to evaluate the efforts of different authors in efficient programming instead of evaluating algorithmic merits.
On the other hand, efficient data management tools like index-structures can show considerable impact on data mining tasks and are therefore useful for a broad variety of algorithms.
In ELKI, data mining algorithms and data management tasks are separated and allow for an independent evaluation. This separation makes ELKI unique among data mining frameworks like Weka or Rapidminer and frameworks for index structures like GiST. At the same time, ELKI is open to arbitrary data types, distance or similarity measures, or file formats. The fundamental approach is the independence of file parsers or database connections, data types, distances, distance functions, and data mining algorithms. Helper classes, e.g. for algebraic or analytic computations are available for all algorithms on equal terms.
With the development and publication of ELKI, we humbly hope to serve the data mining and database research community beneficially. The framework is free for scientific usage ("free" as in "open source", see License for details). In case of application of ELKI in scientific publications, we would appreciate credit in form of a citation of the appropriate publication (see our list of publications), that is, the publication related to the release of ELKI you were using.
Beginners may want to start at the HowTo documents, Examples and Tutorials to help with difficult configuration scenarios and beginning with ELKI development.
This website serves as community development hub and task tracker for both bug reports, Tutorials, FAQ, general issues and development tasks.
You can download ELKI including source code on the Releases page.
ELKI uses the AGPLv3 License, a well-known open source license.
ELKI is quite fast (see some of our benchmark results) but the focus lies on a broad coverage of algorithms and variations. We discourage cross-platform benchmarking, because it is easy to produce misleading results by comparing apples and oranges. For fair comparability, you should implement all algorithms within ELKI, and use the same APIs. We ha...
You can browse the open bug reports or create a new bug report.
We also appreciate any comments, suggestions and code contributions.
•Extensibility - ELKI has a very modular design. We want to allow arbitrary combinations of data types, distance functions, algorithms, input formats, index structures and evaluations methods
•Contributions - ELKI grows only as fast as people contribute. By having a modular design that allows small contributions such as single distance functions and single algorithms, we can have students and external contributors participate in the progress of ELKI
•Completeness - for an exhaustive comparison of methods, we aim at covering as much published and credited work as we can
•Fairness - It is easy to do an unfair comparison by badly implementing a competitor. We try to implement every method as good as we can, and by publishing the source code allow for external improvements. We try to add all proposed improvements, such as index structures for faster range and kNN queries
•Performance - the modular architecture of ELKI allows optimized versions of algorithms and index structures for acceleration
•Progress - ELKI is changing with every release. To accomodate new features and enhance performance, API breakages are unavoidable. We hope to get a stable API with the 1.0 release, but we are not there yet.
ELKI is built using the Gradle wrapper:
will produce a single executable jar file named elki-bundle- .jar.
Individual jar files can be built using:
A complete build (with tests and JavaDoc, it will take a few minutes) can be triggered as:
ElKiKu im Herzen von Blasewitz Brucknerstraße 9 01309 Dresden (Blasewitz) Telefon: 0351 – 4 64 66 69 . Beratung nach der Geburt. Das FABELhafte Eltern-Baby-Konzept: Workshops, Seminare und Vorträge Anne Weidlich www.anneweidlich.de . Elternuniversität der Volkshochschule Dresden. Elternseminare als Hilfen bei der Bewältigung des ...
The tutorial below explains a basic use of ELKI, how to use the MiniGUI and the visualizations. There are additional tutorials available for developing with ELKI. Tutorials for ELKI development: Implementing a custom distance function, a variable exponent Minkowski-norm. Implementing a new outlier detection algorithm, using the distances ...
