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DICE: Density-Based Interactive Clustering and Exploration

MCML Authors

Peer Kröger

Prof. Dr.

Principal Investigator

* Former Principal Investigator

Link to Profile Thomas Seidl PI Matchmaking

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Abstract

Clustering algorithms are mostly following the pipeline to provide input data, and hyperparameter values. Then the algorithms are executed and the output files are generated or visualized. We provide in our work an early prototype of an interactive density-based clustering tool named DICE in which the users can change the hyperparameter settings and immediately observe the resulting clusters. Further the users can browse through each of the single detected clusters and get statistics regarding as well as a convex hull profile for each cluster. Further DICE keeps track of the chosen settings, enabling the user to review which hyperparameter values have been previously chosen. DICE can not only be used in scientific context of analyzing data, but also in didactic settings in which students can learn in an exploratory fashion how a density-based clustering algorithm like e.g. DBSCAN behaves.

inproceedings

BTW 2019

18th Symposium of Database Systems for Business, Technology and Web. Rostock, Germany, Mar 04-08, 2019.

Authors

D. Kazempour • M. Kazakov • P. Kröger • T. Seidl

Links

DOI

Research Area

 A3 | Computational Models

BibTeXKey: KKK+19a

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