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I Fold You So! an Internal Evaluation Measure for Arbitrary Oriented Subspace Clustering Through Piecewise-Linear Approximations of Manifolds

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

In this work we propose SRE, the first internal evaluation measure for arbitrary oriented subspace clustering results. For this purpose we present a new perspective on the subspace clustering task: the goal we formalize is to compute a clustering which represents the original dataset by minimizing the reconstruction loss from the obtained subspaces, while at the same time minimizing the dimensionality as well as the number of clusters. A fundamental feature of our approach is that it is model-agnostic, i.e., it is independent of the characteristics of any specific subspace clustering method. It is scale invariant and mathematically founded. The experiments show that the SRE scoring better assesses the quality of an arbitrarily oriented sub-space clustering compared to commonly used external evaluation measures.

inproceedings

Workshop @ICDM 2020

Workshop at the 20th IEEE International Conference on Data Mining. Sorrento, Italy, Nov 17-20, 2020.

Authors

D. Kazempour • A. Beer • P. Kröger • T. Seidl

Links

DOI

Research Area

 A3 | Computational Models

BibTeXKey: KBK+20

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