A Generator for Subspace Clusters

MCML Authors

Anna Beer

Dr.

* Former Member

→ Group Thomas Seidl
 Database Systems, Data Mining and AI

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Abstract

We introduce a generator for data containing subspace clusters which is accurately tunable and adjustable to the needs of developers. It is online available and allows to give a plethora of characteristics the data should contain, while it is simultaneously able to generate meaningful data containing subspace clusters with a minimum of input data.

inproceedings

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LWDA 2019

Conference on Lernen. Wissen. Daten. Analysen. Berlin, Germany, Sep 30-Oct 02, 2019.

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Authors

A. Beer • N. S. Schüler • T. Seidl

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Links

PDF

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Research Area

 A3 | Computational Models