On Systematic Hyperparameter Analysis Through the Example of Subspace Clustering

MCML Authors

Daniyal Kazempour

Dr.

* Former Member

→ Group Thomas Seidl
 Database Systems, Data Mining and AI

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Abstract

In publications where a clustering method is described, the chosen hyperparameters are in many cases to our current observation empirically determined. In this work in progress we discuss and propose one approach on how hyperparameters can be systematically explored and their effects regarding the data set analyzed. We further introduce in the context of hyperparameter analysis a modified definition of the resilience term, which refers here to a subset of data points which persists to be in the same cluster over different hyperparameter settings. In order to analyze relations among different hyperparameters we further introduce the concept of dynamic intersection computing.

inproceedings

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SSDBM 2019

31st International Conference on Scientific and Statistical Database Management. Santa Cruz, CA, USA, Jul 23-25, 2019.

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Authors

D. Kazempour • T. Seidl

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Links

DOI

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Research Area

 A3 | Computational Models