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Graph Ordering and Clustering - A Circular Approach

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Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Abstract

As the ordering of data, particularly of graphs, can influence the result of diverse Data Mining tasks performed on it heavily, we introduce the Circle-Index, the first internal quality measurement for orderings of graphs. It is based on a circular arrangement of nodes, but takes in contrast to similar arrangements from the field of, e.g., visual analytics, the edge lengths in this arrangement into account. The minimization of the Circle-Index leads to an arrangement which not only offers a simple way to cluster the data using a constrained texttt{MinCut} in only linear time, but is also visually convincing. We developed the clustering algorithm CirClu which implements this minimization and texttt{MinCut}, and compared it with several established clustering algorithms achieving very good results. Simultaneously we compared the Circle-Index with several internal quality measures for clusterings. We observed a strong coherence between the Circle-Index and the matching of achieved clusterings to the respective ground truths in diverse real world datasets.

inproceedings

SSDBM 2019

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

Authors

A. Beer • T. Seidl

Links

DOI

Research Area

 A3 | Computational Models

BibTeXKey: BS19

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