DBHD: Density-based clustering for highly varying density

Durani, Walid; Mautz, Dominik; Plant, Claudia und Böhm, Christian (2022): DBHD: Density-based clustering for highly varying density. 2022 IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, 28 November 2022 - 01 December 2022. In: 2022 IEEE International Conference on Data Mining (ICDM), S. 921-926

Abstract

A major challenge in cluster analysis is the discovery of clusters with widely varying sizes, densities, and shapes. Most clustering algorithms lack the ability to detect heterogeneous clusters that differ greatly in all three properties simultaneously. In this work, we propose the Density Clustering for Highly varying Density algorithm (DBHD). DBHD uses a novel approach that considers local density information and introduces two new conditions to distinguish between different types of data points. Based on this and the adaptively computed density information, DBHD can detect the clusters described above and is robust to noise. Moreover, DBHD has intuitive and robust parameters. In extensive experiments, we show that our technique is considerably more effective in detecting clusters of different shapes, sizes, and densities than well-known (DBSCAN or OPTICS) and recently proposed algorithms such as DPC, SNN-DPC, or LSDBC.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• Sprache: Englisch