Data Mining and Machine Learning

Assoz. Prof. Dipl.-Inform.Univ. Dr. Christian Böhm

Christian Böhm
Research Group Data Mining and Machine Learning
Währinger Straße 29
1090 Wien
Room : 3.35
T: +43-1-4277-79514
christian.boehm(at)univie.ac.at
Personal homepage
u:cris portal

Courses

Publications

Leiber, C, Miklautz, L, Plant, C & Böhm, C 2025, 'An Introductory Survey to Autoencoder-based Deep Clustering - Sandboxes for Combining Clustering with Deep Learning', CoRR, vol. abs/25. <http://eprints.cs.univie.ac.at/8417/>

Guo, W, Ye, W, Chen, C, Sun, X, Böhm, C, Plant, C & Rahardja, S 2025, 'Bootstrap Deep Spectral Clustering with Optimal Transport', IEEE Transactions on Multimedia. <http://eprints.cs.univie.ac.at/8642/>

Durani, W, Mautz, D, Plant, C & Böhm, C 2025, DMDHC: Discovery of Multi-Density Hierarchical Cluster Structures. in V Papalexakis, M Riondato, E Zheleva, T Weninger & W Ding (eds), Proceedings of the 2025 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, pp. 261-269. https://doi.org/10.1137/1.9781611978520.25

Li, P, Wang, H & Böhm, C 2025, Scalable Graph Classification via Random Walk Fingerprints (Extended Abstract). in IJCAI. ijcai.org, pp. 10912-10915. <http://eprints.cs.univie.ac.at/8643/>

Atria, CE, Cai, Y, Weber, P, Beer, A, Kriege, NM, Böhm, C, Revilla Domingo, R & Plant, C 2025, 'sc-GRIP: a Graph Convolutional Approach to Infer Gene Interaction Polarity from Single-cell Data', Paper presented at International Conference on Data Mining, Washington DC, United States, 12/11/25 - 15/11/25.

Durani, W, Nitzl, T, Plant, C & Böhm, C 2025, 'Weakly Supervised Anomaly Detection via Dual-Tailed Kernel.'. <https://dblp.org/rec/conf/icml/DuraniNP025>

Beer, A, Weber, P, Miklautz, L, Leiber, C, Durani, W, Böhm, C & Claudia, P 2024, SHADE: Deep Density-based Clustering. in IEEE International Conference on Data Mining (ICDM) 2024. <http://eprints.cs.univie.ac.at/8279/>

Beer, A, Weber, P, Miklautz, L, Leiber, C, Durani, W, Böhm, C & Plant, C 2024 'SHADE: Deep Density-based Clustering' arXiv. https://doi.org/10.48550/ARXIV.2410.06265

Qian, L, Qian, J, Sun, X, Guo, W & Böhm, C 2024, ADOD: Adaptive Density Outlier Detection. in IEEE International Conference on Data Mining, ICDM 2024, Abu Dhabi, United Arab Emirates, December 9-12, 2024. IEEE, pp. 400-409. <http://eprints.cs.univie.ac.at/8370/>

Qian, L, Plant, C, Qin, Y, Qian, J & Böhm, C 2024, DynoGraph: Dynamic Graph Construction for Nonlinear Dimensionality Reduction. in IEEE International Conference on Data Mining, ICDM 2024, Abu Dhabi, United Arab Emirates, December 9-12, 2024. IEEE, pp. 827-832. <http://eprints.cs.univie.ac.at/8371/>

Li, P, Wang, H & Böhm, C 2024, Scalable Graph Classification via Random Walk Fingerprints. in ICDM. IEEE, pp. 231-240. <http://eprints.cs.univie.ac.at/8495/>

Han, W, Qin, Z, Liu, J, Böhm, C & Shao, J 2024, 'Synchronization-Inspired Interpretable Neural Networks', IEEE transactions on neural networks and learning systems, vol. 35, no. 11, pp. 16762-16774. https://doi.org/10.1109/TNNLS.2023.3297672

Lüer, F, Weber, T, Dolgich, M & Böhm, C 2023, Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores. in IEEE International Conference on Data Mining (2023). <http://eprints.cs.univie.ac.at/7865/>

Leiber, C, Miklautz, L, Plant, C & Böhm, C 2023, Application of Deep Clustering Algorithms. in CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. ACM, pp. 5208-5211. https://doi.org/10.1145/3583780.3615290

Bauer, LGM, Leiber, C, Böhm, C & Plant, C 2023, Extension of the Dip-test Repertoire - Efficient and Differentiable p-value Calculation for Clustering. in Proceedings of the 2023 SIAM International Conference on Data Mining, SDM 2023, Minneapolis-St. Paul Twin Cities, MN, USA, April 27-29, 2023. SIAM, pp. 109-117. <http://eprints.cs.univie.ac.at/7934/>

Sun, X, Song, Z, Yu, Y, Dong, J, Plant, C & Böhm, C 2023, 'Network Embedding via Deep Prediction Model', IEEE Transactions on Big Data, vol. 9, no. 2, pp. 455-470. https://doi.org/10.1109/TBDATA.2022.3194643

Leiber, C, Miklautz, L, Plant, C & Böhm, C 2023, Benchmarking Deep Clustering Algorithms With ClustPy. in J Wang, Y He, TN Dinh, C Grant, M Qiu & W Pedrycz (eds), Proceedings - 23rd IEEE International Conference on Data Mining Workshops: ICDMW 2023. IEEE, pp. 625-632. https://doi.org/10.1109/ICDMW60847.2023.00087

Ye, W, Mautz, D, Böhm, C, Singh, AK & Plant, C 2023, Incorporating User's Preference into Attributed Graph Clustering : Extended abstract. in 39th IEEE International Conference on Data Engineering, ICDE 2023, Anaheim, CA, USA, April 3-7, 2023. IEEE, pp. 3833-3834. https://doi.org/10.1109/ICDE55515.2023.00343

Li, P, Wang, H, Li, K & Böhm, C 2023, 'Influence without Authority: Maximizing Information Coverage in Hypergraphs.', Paper presented at SIAM International Conference on Data Mining (SDM23), Minneapolis, United States, 27/04/23 - 29/04/23 pp. 10-18.

Li, P, Pan, L, Li, K, Plant, C & Böhm, C 2023, Interpretable Subgraph Feature Extraction for Hyperlink Prediction. in IEEE International Conference on Data Mining, ICDM 2023, Shanghai, China, December 1-4, 2023. IEEE, pp. 279-288. <http://eprints.cs.univie.ac.at/8068/>

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Talks

Sc-GRIP: a Graph Convolutional Approach to Infer Gene Interaction Polarity from Single-Cell Data

Yitao Cai , Carolina Elisabeth Atria , Pascal Weber , Anna Beer , Nils Morten Kriege , Christian Böhm , Roger Revilla Domingo , Claudia Plant
41st TBI Winterseminar in Bled
Conference, Talk or oral contribution
8.2.2026

Application of Deep Clustering Algorithms

Collin Leiber , Lukas Miklautz , Claudia Plant , Christian Böhm
32nd ACM International Conference on Information and Knowledge Management
Conference, Talk or oral contribution
21.10.2023
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