LUCKe- Connecting Clustering and Correlation Clustering

MCML Authors

Anna Beer

Dr.

* Former Member

→ Group Thomas Seidl
 Database Systems, Data Mining and AI

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Abstract

LUCKe allows any purely distance-based 'classic' clustering algorithm to reliably find linear correlation clusters. An elaborated distance matrix based on the points’ local PCA extracts all necessary information from high dimensional data to declare points of the same arbitrary dimensional linear correlation cluster as 'similar'. For that, the points’ eigensystems as well as only the relevant information about their position in space, are put together. LUCKe allows transferring known benefits from the large field of basic clustering to correlation clustering. Its applicability is shown in extensive experiments with simple representatives of diverse basic clustering approaches.

inproceedings

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Workshop @ICDM 2021

Workshop at the 21st IEEE International Conference on Data Mining. Auckland, New Zealand, Dec 07-10, 2021.

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Authors

A. Beer • L. Stephan • T. Seidl

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Links

DOI

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Research Area

 A3 | Computational Models