ELKIOverview, Comparison, & Features
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
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.