On Methods and Measures for the Inspection of Arbitrarily Oriented Subspace Clusters

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

When using arbitrarily oriented subspace clustering algorithms one obtains a partitioning of a given data set and for each partition its individual subspace. Since clustering is an unsupervised machine learning task, we may not have “ground truth” labels at our disposal or do not wish to rely on them. What is needed in such cases are internal measure which permits a label-less analysis of the obtained subspace clustering. In this work, we propose methods for revising clusters obtained from arbitrarily oriented correlation clustering algorithms. Initial experiments conducted reveal improvements in the clustering results compared to the original clustering outcome. Our proposed approach is simple and can be applied as a post-processing step on arbitrarily oriented correlation clusterings.

article

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Datenbank-Spektrum

21. Sep. 2021.

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Authors

D. Kazempour • J. Winter • P. Kröger • T. Seidl

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Links

DOI

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Research Area

 A3 | Computational Models