Home | Publications | BKS+19a

LUCK - Linear Correlation Clustering Using Cluster Algorithms and a KNN Based Distance Function (Short Paper)

MCML Authors

Link to Profile Thomas Seidl

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Abstract

LUCK allows to use any distance-based clustering algorithm to find linear correlated data. For that a novel distance function is introduced, which takes the distribution of the kNN of points into account and corresponds to the probability of two points being part of the same linear correlation. In this work in progress we tested the distance measure with DBSCAN and k-Means comparing it to the well-known linear correlation clustering algorithms ORCLUS, 4C, COPAC, LMCLUS, and CASH, receiving good results for difficult synthetic data sets containing crossing or non-continuous correlations.

inproceedings BKS+19a

SSDBM 2019

31st International Conference on Scientific and Statistical Database Management. Santa Cruz, CA, USA, Jul 23-25, 2019.

Authors

A. Beer • D. Kazempour • L. Stephan • T. Seidl

Links

DOI

Research Area

 A3 | Computational Models

BibTeXKey: BKS+19a

#p-seidl
Back to Top