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2020 – today
- 2023
[j12]Quirin Stier
, Michael C. Thrun:
Deriving Homogeneous Subsets from Gene Sets by Exploiting the Gene Ontology. Informatica 34(2): 357-386 (2023)- [j11]Michael C. Thrun, Julian Märte, Quirin Stier:
Analyzing Quality Measurements for Dimensionality Reduction. Mach. Learn. Knowl. Extr. 5(3): 1076-1118 (2023) - 2022
- [j10]Michael C. Thrun:
Exploiting Distance-Based Structures in Data Using an Explainable AI for Stock Picking. Inf. 13(2): 51 (2022) - [j9]Michael C. Thrun:
Identification of Explainable Structures in Data with a Human-in-the-Loop. Künstliche Intell. 36(3): 297-301 (2022) - [c8]Michael C. Thrun, Quirin Stier, Alfred Ultsch:
Interactive Toolbox for Two-Dimensional Gaussian Mixture Modeling. ECML/PKDD (6) 2022: 658-661 - [c7]Michael C. Thrun:
Knowledge-Based Identification of Homogenous Structures in Gene Sets. WorldCIST (1) 2022: 81-90 - 2021
- [j8]Michael C. Thrun, Alfred Ultsch:
Swarm intelligence for self-organized clustering. Artif. Intell. 290: 103237 (2021) - [j7]Michael C. Thrun, Alfred Ultsch:
Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data. J. Classif. 38(2): 280-312 (2021) - [j6]Michael C. Thrun:
The Exploitation of Distance Distributions for Clustering. Int. J. Comput. Intell. Appl. 20(3): 2150016:1-2150016:19 (2021) - [j5]Michael C. Thrun, Felix Pape, Alfred Ultsch:
Conventional displays of structures in data compared with interactive projection-based clustering (IPBC). Int. J. Data Sci. Anal. 12(3): 249-271 (2021) - [j4]Michael C. Thrun, Felix Pape, Alfred Ultsch:
Correction to: Conventional displays of structures in data compared with interactive projection-based clustering (IPBC). Int. J. Data Sci. Anal. 12(4): 405 (2021) - [j3]Michael C. Thrun, Alfred Ultsch, Lutz Breuer:
Explainable AI Framework for Multivariate Hydrochemical Time Series. Mach. Learn. Knowl. Extr. 3(1): 170-204 (2021) - [j2]Michael C. Thrun, Quirin Stier:
Fundamental clustering algorithms suite. SoftwareX 13: 100642 (2021) - [i4]Michael C. Thrun, Alfred Ultsch:
Swarm Intelligence for Self-Organized Clustering. CoRR abs/2106.05521 (2021) - [i3]Alfred Ultsch, Jörg Hoffmann, Maximilian Röhnert, Malte Von Bonin, Uta Oelschlägel, Cornelia Brendel, Michael C. Thrun:
An Explainable AI System for the Diagnosis of High Dimensional Biomedical Data. CoRR abs/2107.01820 (2021) - [i2]Michael C. Thrun:
The Exploitation of Distance Distributions for Clustering. CoRR abs/2108.09649 (2021) - 2020
- [j1]Pedro A. López-García, Denisse L. Argote, Michael C. Thrun:
Projection-Based Classification of Chemical Groups for Provenance Analysis of Archaeological Materials. IEEE Access 8: 152439-152451 (2020) - [c6]Michael C. Thrun, Felix Pape, Alfred Ultsch:
Interactive Machine Learning Tool for Clustering in Visual Analytics. DSAA 2020: 479-487 - [c5]Michael C. Thrun, Alfred Ultsch:
Swarm Intelligence for Self-Organized Clustering (Extended Abstract). IJCAI 2020: 5125-5129 - [c4]Michael C. Thrun, Alfred Ultsch:
Swarm-Based Cluster Analysis for Knowledge Discovery. KI 2020: 240-244 - [c3]Michael C. Thrun:
Improving the Sensitivity of Statistical Testing for Clusterability with Mirrored-Density Plots. MLVis@Eurographics/EuroVis 2020: 19-23
2010 – 2019
- 2019
- [c2]Michael C. Thrun:
Knowledge Discovery in Quarterly Financial Data of Stocks Based on the Prime Standard using a Hybrid of a Swarm with SOM. ESANN 2019 - [i1]Michael C. Thrun, Tino Gehlert, Alfred Ultsch:
Analyzing the Fine Structure of Distributions. CoRR abs/1908.06081 (2019) - 2018
- [b1]Michael C. Thrun:
Projection-Based Clustering through Self-Organization and Swarm Intelligence, Combining Cluster Analysis with the Visualization of High-Dimensional Data. University of Marburg, Germany, Springer Vieweg 2018, ISBN 978-3-658-20539-3, pp. 1-162 - 2017
- [c1]Alfred Ultsch, Michael C. Thrun:
Credible visualizations for planar projections. WSOM 2017: 77-81
Coauthor Index
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