ELKI

​Overview, Comparison, & Features​

ELKI is a data mining software framework developed for use in research and teaching. 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


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Learn What ELKI Can Do For You

The object-oriented architecture allows the combination of arbitrary algorithms, data types, distance functions, indexes, and evaluation measures. The Java just-in-time-compiler optimizes all combinations to an identical extent, making benchmarking results more comparable if they share large parts of the code. The focus of ELKI is research in algorithms, with stress on unsupervised methods in cluster analysis and outlier detection. To realize high performance and scalability, ELKI offers data index structures like the R*-tree which will provide major performance gains.

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ELKI

Key Modules:

First lets understand what to expect from this software in a general overview.

Features:

  • Minimalistic graphical interface for interactive parameterization of ELKI algorithms.
  • Data mining algorithms such as k-means variations, outlier detection ensembles.
  • Visualize results:
  • Outlier Scores.
  • Clustering results

  • Histograms.

  • ROC Curves.

  • Index structures (various R-tree splitting and bulk loading strategies).

  • Evaluation methods (various clustering similarity measures).

  • Spatial outlier detection visualization on geographical data.

Pros & Cons:

Pricing:

Pros:

  • Extensibility
  • Development Progress

ELKI provides custom-based pricing.  


Why Choose ELKI?

A key concept of ELKI is to permit the mixture 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. In ELKI, data mining algorithms and data management tasks are separated and allow for an independent evaluation. This separation makes ELKI unique among data processing frameworks like Weka or Rapidminer and frameworks for index structures like GiST.

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FAQ

How to does ELKI login?
Simple and efficient tools for predictive data analysis · Accessible to everybody, and reusable in various contexts · To login with ELKI  visit https://www.elki.com/store/login
How to install ELKI?
Installation for Linux/Mac OS
Download a stable release from ELKI download page and unzip the file. You can use the "wget" command as: wget -O elki . zip.
How do I use ELKI?
Just run the jar file, either by double-clicking it or via command linejava -jar elki.jar. Obviously, you need to have a Java Runtime Environment installed. A window should open that assists you with parameterizing all algorithms released with ELKI.
Is there a complete list of parameters for ELKI?
Of course. It can be found in the JavaDoc of the release that you are using. Here are the lists for the current release sorted by class name and sorted by option name. The list is automatically generated from the parameterization classes, so it is fairly complete.
How can I contact with Data Melt technical team?

Compliance Learn about the Data Melt security features and there compliance recommendations. Need more help? you can visit https://meltmedia.com/contact

How to uninstall ELKI?
  1. To remove a package such as ELKI in an environment such as myenv: conda remove -n myenv data elki.
  2. To remove a package such as ELKI in the current environment: conda remove data elki.
  3. To remove multiple packages at once, such as ELKI and cURL
  4. To confirm that a package has been removed
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