Michael C. Thrun

According to our database, Michael C. Thrun authored at least 22 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Deriving Homogeneous Subsets from Gene Sets by Exploiting the Gene Ontology.

Quirin Stier, Michael C. Thrun

Informatica, 2023

2022

Identification of Explainable Structures in Data with a Human-in-the-Loop.

Michael C. Thrun

Künstliche Intell., 2022

Exploiting Distance-Based Structures in Data Using an Explainable AI for Stock Picking.

Michael C. Thrun

Inf., 2022

Knowledge-Based Identification of Homogenous Structures in Gene Sets.

Michael C. Thrun

Proceedings of the Information Systems and Technologies, 2022

Interactive Toolbox for Two-Dimensional Gaussian Mixture Modeling.

Michael C. Thrun, Quirin Stier, Alfred Ultsch

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

2021

Fundamental clustering algorithms suite.

Michael C. Thrun, Quirin Stier

SoftwareX, 2021

Explainable AI Framework for Multivariate Hydrochemical Time Series.

Michael C. Thrun, Alfred Ultsch, Lutz Breuer

Mach. Learn. Knowl. Extr., 2021

Correction to: Conventional displays of structures in data compared with interactive projection-based clustering (IPBC).

Michael C. Thrun, Felix Pape, Alfred Ultsch

Int. J. Data Sci. Anal., 2021

Conventional displays of structures in data compared with interactive projection-based clustering (IPBC).

Michael C. Thrun, Felix Pape, Alfred Ultsch

Int. J. Data Sci. Anal., 2021

The Exploitation of Distance Distributions for Clustering.

Michael C. Thrun

Int. J. Comput. Intell. Appl., 2021

An Explainable AI System for the Diagnosis of High Dimensional Biomedical Data.

Alfred Ultsch, Jörg Hoffmann, Maximilian Röhnert, Malte Von Bonin, Uta Oelschlägel, Cornelia Brendel, Michael C. Thrun

CoRR, 2021

Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data.

Michael C. Thrun, Alfred Ultsch

J. Classif., 2021

Swarm intelligence for self-organized clustering.

Michael C. Thrun, Alfred Ultsch

Artif. Intell., 2021

2020

Projection-Based Classification of Chemical Groups for Provenance Analysis of Archaeological Materials.

Pedro A. López-García, Denisse L. Argote, Michael C. Thrun

IEEE Access, 2020

Improving the Sensitivity of Statistical Testing for Clusterability with Mirrored-Density Plots.

Michael C. Thrun

Proceedings of the 3rd Workshop on Machine Learning Methods in Visualisation for Big Data, 2020

Swarm-Based Cluster Analysis for Knowledge Discovery.

Michael C. Thrun, Alfred Ultsch

Proceedings of the KI 2020: Advances in Artificial Intelligence, 2020

Swarm Intelligence for Self-Organized Clustering (Extended Abstract).

Michael C. Thrun, Alfred Ultsch

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Interactive Machine Learning Tool for Clustering in Visual Analytics.

Michael C. Thrun, Felix Pape, Alfred Ultsch

Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

2019

Analyzing the Fine Structure of Distributions.

Michael C. Thrun, Tino Gehlert, Alfred Ultsch

CoRR, 2019

Knowledge Discovery in Quarterly Financial Data of Stocks Based on the Prime Standard using a Hybrid of a Swarm with SOM.

Michael C. Thrun

Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

2018

Projection-Based Clustering through Self-Organization and Swarm Intelligence, Combining Cluster Analysis with the Visualization of High-Dimensional Data.

Michael C. Thrun

Springer Vieweg, ISBN: 978-3-658-20539-3, 2018

2017

Credible visualizations for planar projections.

Alfred Ultsch, Michael C. Thrun

Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, 2017