Lehr- und Forschungseinheit für Datenbanksysteme

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Collin Leiber

Collin Leiber

Research Assistant


Ludwig-Maximilians-Universität München
Lehrstuhl für Datenbanksysteme und Data Mining
Oettingenstraße 67
80538 München

Room: F 110
Phone: +49-89-2180-9304


Research Interests

  • Estimating the Number of Clusters
  • Hartigan's Dip-Test of Unimodality
  • Deep Clustering
  • (Common) Subspace Clustering
  • Non-Redundant/Alternative Clustering
  • Information Theoretic Clustering

Supervised Theses


  • Numerical Algorithms for Symmetric Eigenvalue Decomposition in Subspace k-means Clustering
  • Density-Based Clustering: A Robust Multi-Density Alternative to DBSCAN
  • Improving the FOSSCLU Algorithm:Optimized Parameter-search and Clustering in Non-redundant Subspaces
  • Ein nicht-parametrischer Ansatz zur Erkennung beliebiger Clusterstrukturen
  • Automated tumor detection based on routine blood tests using Artificial Neural Networks
  • Improving Non-Redundant Clustering Approaches by K-means Extensions
  • Acquiring Meaningful Data Representations by Combining Autoencoders and Hartigan's Dip Test
  • Parameterfreies nicht-redundantes Clustering mithilfe des Dip-Tests
  • Self-Supervised Deep Clustering for Tabular Data
  • Design and Implementation of a Database with Web Interface for Side Effects of Immunotherapies
  • Evaluation of Density Based Mode Seeking
  • MDL-based Parameter Selection for Subspace and Non-Redundant Clustering


  • Informationsextraktion aus Einverständniserklärungen von klinischen Studien mittels Natural Language Processing und Maschinellem Lernen
  • Deep Learning-based Forensic Age Estimation from CT Images
  • Eine Prognose der COVID-19-Fallzahlen in Deutschland durch Neuronale Netze basierend auf Veränderungen der Mobilität
  • Minimal Description Length Principle for Parameter-free Motif Detection in Time Series Data
  • k-SubMix: Clustering on mixed-type data in optimal Subspaces
  • Subspace Clustering nicht-konvexer Strukturen durch Micro-Cluster Graphoptimierung
  • A Survey of Unimodality Tests and their Potential in Clustering Environments
  • Uncertainty Quantification in Deep Learning-Based Forensic Age Estimation
  • Deep Density-Based Clustering using Contrastive Learning
  • Building Decision Trees using Hartigans Dip-Test of Unimodality


  • Leiber, Collin* and Bauer, Lena G. M.* and Schelling, Benjamin and Böhm, Christian and Plant, Claudia
    Dip-based Deep Embedded Clustering with k-Estimation
    KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021, pp. 903-913, ACM, 2021. [DOI][Code]
  • Leiber, Collin and Mautz, Dominik and Plant, Claudia and Böhm, Christian
    Automatic Parameter Selection for Non-Redundant Clustering
    Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2022. [DOI][Supplement][Code]
  • Leiber, Collin and Bauer, Lena G. M. and Neumayr, Michael and Plant, Claudia and Böhm, Christian
    The DipEncoder: Enforcing Multimodality in Autoencoders
    KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14-18, 2022, ACM, 2022. [DOI][Code]
  • Bauer, Lena G. M.* and Leiber, Collin* and Böhm, Christian and Plant, Claudia
    Extension of the Dip-test Repertoire - Efficient and Differentiable p-value Calculation for Clustering
    Proceedings of the 2023 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2023. [DOI][Code + Supplement]
  • Klein, Mauritius and Leiber, Collin and Böhm, Christian
    k-SubMix: Common Subspace Clustering on Mixed-Type Data
    Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023, 2023. [DOI][Code]
  • Miklautz, Lukas and Shkabrii, Andrii and Leiber, Collin and Tobias, Bendeguz and Seidl, Benedict and Weissensteiner, Elisabeth and Rausch, Andreas and Böhm, Christian and Plant, Claudia
    Non-Redundant Image Clustering of Early Medieval Glass Beads
    IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023. [DOI]
  • Leiber, Collin and Miklautz, Lukas and Plant, Claudia and Böhm, Christian
    Application of Deep Clustering Algorithms
    Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023. [DOI]