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ELKI
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ELKI

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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. A key concept of ELKI is to allow the combination of arbitrary algorithms, data types, distance functions and indexes and evaluate these combinations.

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Data Mining Software

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7.5
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7.5
Advanced Features
7.5
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7.5
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7.5
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7.5
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ELKI is modeled around a database core, which uses a vertical data layout that stores data in column groups (similar to column families in NoSQL databases).

7.5
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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. A key concept of ELKI is to allow the combination 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.

ELKI is modeled around a database core, which uses a vertical data layout that stores data in column groups (similar to column families in NoSQL databases). This database core provides nearest neighbor search, range/radius search, and distance query functionality with index acceleration for a wide range of dissimilarity measures. Algorithms based on such queries (e.g. k-nearest-neighbor algorithm, local outlier factor and DBSCAN) can be implemented easily and benefit from the index acceleration. The database core also provides fast and memory efficient collections for object collections and associative structures such as nearest neighbor lists.

The visualization module uses SVG for scalable graphics output, and Apache Batik for rendering of the user interface as well as lossless export into PostScript and PDF for easy inclusion in scientific publications in LaTeX. Exported files can be edited with SVG editors such as Inkscape.

ELKI

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Ease of use
Features & Functionality
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