LUCKe - Connecting Clustering and Correlation Clustering

Anna Beer, Lisa Stephan, Thomas Seidl

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review


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.

Original languageEnglish
Title of host publicationProceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021
Number of pages10
Publication date2021
ISBN (Electronic)9781665424271
Publication statusPublished - 2021


  • PCA
  • clustering
  • linear correlation clustering


Dive into the research topics of 'LUCKe - Connecting Clustering and Correlation Clustering'. Together they form a unique fingerprint.

Cite this