-
- Description:
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 also includes the source code, since this software is meant for the rapid development of such algorithms, not so much for end users.
- Changes to previous version:
Additions and improvements from ELKI 0.7.0 to 0.7.1:
Algorithm additions:
GriDBSCAN: DBSCAN using grid partitioning (Minkowski distances only)
Compare-Means and Sort-Means k-means variations (much faster than traditional k-means)
Visualization of dendrograms.
Important bug fixes:
Classes with no package ("default package") would cause errors.
The fast power function implementation was sometimes returning incorrect results.
Random sampling was sometimes not sampling from the full data set.
UI improvements:
The file input source will now automatically choose the Arff parser for .arff files.
MiniGUI now allows choosing other applications.
MiniGUI now displays the command line in a separate field.
MiniGUI displays an error message, if an incorrect classpath or JAyatana (on Ubuntu) is detected.
Export to png now works, we added a work-around for an open Batik bug.
Smaller changes:
Many smaller bug fixes.
C-Index for cluster evaluation now can process larger data sets.
OPTICS output of undefined reachability fixed.
External distance matrixes are easier to use and perform additional checks.
Precomputed distance matrixes can answer range and kNN queries.
Voronoi visualization can be switched in the menu now.
Improved backwards command line compatibility with additional aliases.
Added generated @since annotations in JavaDoc.
Many new unit tests, renamed to the Java conventions.
Low-level reading of service files, to have faster startup.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- Supported Operating Systems: Platform Independent
- Data Formats: Arff, Other, Csv, Parser Extension Api
- Tags: Clustering, Visualization, Algorithms, Evaluation, Anomaly Detection, Outlier Detection, Index Structures
- Archive: download here
Comments
No one has posted any comments yet. Perhaps you'd like to be the first?
Leave a comment
You must be logged in to post comments.