---
Type: desktop-application
ID: elki.desktop
Package: elki
Name:
C: ELKI
Summary:
C: Data mining algorithm development framework
Description:
fr: >-
<p>ELKI : « Environnement pour le Développement d'applications KDD prises en charge par les structures d'Index »
est un environnement applicatif de développement pour les algorithmes d'exploration de données, écrit en Java. Il
comprend une large gamme d'algorithmes célèbres d'exploration de données, de fonctions de distance et de structures
d'index.</p>
<p>L'accent est mis particulièrement sur les méthodes de détection de regroupements et de valeurs aberrantes, contrairement
à de nombreux autres outils d'exploration de données qui se concentrent sur la classification. En outre, il prend
en charge les structures d'index pour améliorer les performances d'algorithmes tels que R *-Tree et M-Tree.</p>
<p>L'architecture modulaire est conçue pour permettre l'ajout de composants personnalisés tels que les fonctions
ou algorithmes de distance, tout en étant capable de réutiliser les autres parties pour évaluation.</p>
<p>This package contains the compiled ELKI version, and launcher scripts.</p>
en_AU: >-
<p>ELKI: "Environment for Developing KDD-Applications Supported by Index- Structures" is a development framework
for data mining algorithms written in Java. It includes a large variety of popular data mining algorithms, distance functions
and index structures.</p>
<p>Its focus is particularly on clustering and outlier detection methods, in contrast to many other data mining toolkits
that focus on classification. Additionally, it includes support for index structures to improve algorithm performance
such as R*-Tree and M-Tree.</p>
<p>The modular architecture is meant to allow adding custom components such as distance functions or algorithms, while
being able to reuse the other parts for evaluation.</p>
<p>This package contains the compiled ELKI version, and launcher scripts.</p>
C: >-
<p>ELKI: "Environment for Developing KDD-Applications Supported by Index-Structures" is a development framework
for data mining algorithms written in Java. It includes a large variety of popular data mining algorithms, distance functions
and index structures.</p>
<p>Its focus is particularly on clustering and outlier detection methods, in contrast to many other data mining toolkits
that focus on classification. Additionally, it includes support for index structures to improve algorithm performance
such as R*-Tree and M-Tree.</p>
<p>The modular architecture is meant to allow adding custom components such as distance functions or algorithms, while
being able to reuse the other parts for evaluation.</p>
<p>This package contains the compiled ELKI version, and launcher scripts.</p>
en_GB: >-
<p>ELKI: "Environment for Developing KDD-Applications Supported by Index- Structures" is a development framework
for data mining algorithms written in Java. It includes a large variety of popular data mining algorithms, distance functions
and index structures.</p>
<p>Its focus is particularly on clustering and outlier detection methods, in contrast to many other data mining toolkits
that focus on classification. Additionally, it includes support for index structures to improve algorithm performance
such as R*-Tree and M-Tree.</p>
<p>The modular architecture is meant to allow adding custom components such as distance functions or algorithms, while
being able to reuse the other parts for evaluation.</p>
<p>This package contains the compiled ELKI version, and launcher scripts.</p>
en_CA: >-
<p>ELKI: "Environment for Developing KDD-Applications Supported by Index- Structures" is a development framework
for data mining algorithms written in Java. It includes a large variety of popular data mining algorithms, distance functions
and index structures.</p>
<p>Its focus is particularly on clustering and outlier detection methods, in contrast to many other data mining toolkits
that focus on classification. Additionally, it includes support for index structures to improve algorithm performance
such as R*-Tree and M-Tree.</p>
<p>The modular architecture is meant to allow adding custom components such as distance functions or algorithms, while
being able to reuse the other parts for evaluation.</p>
<p>This package contains the compiled ELKI version, and launcher scripts.</p>
gl: >-
<p>ELKI: "Environment for Developing KDD-Applications Supported by Index- Structures" is a development framework
for data mining algorithms written in Java. It includes a large variety of popular data mining algorithms, distance functions
and index structures.</p>
<p>Its focus is particularly on clustering and outlier detection methods, in contrast to many other data mining toolkits
that focus on classification. Additionally, it includes support for index structures to improve algorithm performance
such as R*-Tree and M-Tree.</p>
<p>The modular architecture is meant to allow adding custom components such as distance functions or algorithms, while
being able to reuse the other parts for evaluation.</p>
<p>This package contains the compiled ELKI version, and launcher scripts.</p>
en: >-
<p>ELKI: "Environment for Developing KDD-Applications Supported by Index-Structures" is a development framework
for data mining algorithms written in Java. It includes a large variety of popular data mining algorithms, distance functions
and index structures.</p>
<p>Its focus is particularly on clustering and outlier detection methods, in contrast to many other data mining toolkits
that focus on classification. Additionally, it includes support for index structures to improve algorithm performance
such as R*-Tree and M-Tree.</p>
<p>The modular architecture is meant to allow adding custom components such as distance functions or algorithms, while
being able to reuse the other parts for evaluation.</p>
<p>This package contains the compiled ELKI version, and launcher scripts.</p>
Categories:
- Development
- Education
- Science
- ArtificialIntelligence
- ComputerScience
- DataVisualization
Icon:
cached:
- name: elki_elki-icon.png
width: 64
height: 64
- name: elki_elki-icon.png
width: 128
height: 128
