LUCKe - Connecting Clustering and Correlation Clustering

Anna Beer, Lisa Stephan, Thomas Seidl

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.