elki 0.7.1-10.1 source package in Ubuntu
Changelog
elki (0.7.1-10.1) unstable; urgency=medium
* Non-maintainer upload.
[ Santiago Vila ]
* Fix FTBFS (Timeout on instantiating de.lmu.ifi.dbs.elki.gui.util.TreePopup)
(Closes: #923841).
-- Andrej Shadura <email address hidden> Sat, 09 Mar 2019 22:48:46 +0000
Upload details
- Uploaded by:
- Erich Schubert
- Uploaded to:
- Sid
- Original maintainer:
- Erich Schubert
- Architectures:
- all
- Section:
- science
- Urgency:
- Medium Urgency
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| elki_0.7.1-10.1.dsc | 1.8 KiB | 9ec02cce76104263d736c39c2d129aa1e5b2887b42401416c58cd0cf72a2fca8 |
| elki_0.7.1.orig.tar.gz | 4.3 MiB | ea822a5eafe0fe6719bd92a37131b773ba9fdd1af5d5bac6c977561a0032bd7a |
| elki_0.7.1-10.1.debian.tar.xz | 24.1 KiB | 7925a82b604fd84872b9a7db9d1828ba51f6d3f96f6009f39bb49a64f87601a8 |
Available diffs
- diff from 0.7.1-10 to 0.7.1-10.1 (1.1 KiB)
No changes file available.
Binary packages built by this source
- elki: Data mining algorithm development framework
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.
.
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.
.
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.
.
This package contains the compiled ELKI version, and launcher scripts.
- elki-dev: Data mining algorithm development framework - development files
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.
.
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.
.
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.
.
This package contains the JavaDoc and the source code package.
