---
Type: desktop-application
ID: elki.desktop
Package: elki
Name:
C: ELKI
Summary:
C: Data mining algorithm development framework
Description:
da: >-
<p>ELKI: »Environment for Developing KDD-Applications Supported by Index-Structures« er en udviklingsramme for datamineringsalgoritmer
skrevet i Java. Det inkluderer en bred vifte af populære datamineringsalgortimer, afstandsfunktioner og indeksstrukturer.</p>
<p>Dets fokus er specifikt på klynge- og outlier-detektionsmetoder, i kontrast til mange andre værktøjssæt til dataminering,
som fokuserer på klassifikation. Derudover er der inkluderet understøttelse for indeksstrukturer for at forbedre algoritmeydelsen
såsom R*-Tree og M-Tree.</p>
<p>Den modulære arkitektur er lavet for at tillade tilføjelse af tilpassede komponenter såsom afstandsfunktioner eller
algoritmer, mens den kan genbruge de andre dele til evaluering.</p>
<p>Denne pakke indeholder den kompilerede ELKI-version og opstartsskripter.</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: >-
<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
