Michael C. Thrun

Orcid: 0000-0001-9542-5543

According to our database1, Michael C. Thrun authored at least 23 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2023
Analyzing Quality Measurements for Dimensionality Reduction.
Mach. Learn. Knowl. Extr., June, 2023

Deriving Homogeneous Subsets from Gene Sets by Exploiting the Gene Ontology.
Informatica, 2023

2022
Identification of Explainable Structures in Data with a Human-in-the-Loop.
Künstliche Intell., 2022

Exploiting Distance-Based Structures in Data Using an Explainable AI for Stock Picking.
Inf., 2022

Knowledge-Based Identification of Homogenous Structures in Gene Sets.
Proceedings of the Information Systems and Technologies, 2022

Interactive Toolbox for Two-Dimensional Gaussian Mixture Modeling.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

2021
Fundamental clustering algorithms suite.
SoftwareX, 2021

Explainable AI Framework for Multivariate Hydrochemical Time Series.
Mach. Learn. Knowl. Extr., 2021

Correction to: Conventional displays of structures in data compared with interactive projection-based clustering (IPBC).
Int. J. Data Sci. Anal., 2021

Conventional displays of structures in data compared with interactive projection-based clustering (IPBC).
Int. J. Data Sci. Anal., 2021

The Exploitation of Distance Distributions for Clustering.
Int. J. Comput. Intell. Appl., 2021

An Explainable AI System for the Diagnosis of High Dimensional Biomedical Data.
CoRR, 2021

Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data.
J. Classif., 2021

Swarm intelligence for self-organized clustering.
Artif. Intell., 2021

2020
Projection-Based Classification of Chemical Groups for Provenance Analysis of Archaeological Materials.
IEEE Access, 2020

Improving the Sensitivity of Statistical Testing for Clusterability with Mirrored-Density Plots.
Proceedings of the 3rd Workshop on Machine Learning Methods in Visualisation for Big Data, 2020

Swarm-Based Cluster Analysis for Knowledge Discovery.
Proceedings of the KI 2020: Advances in Artificial Intelligence, 2020

Swarm Intelligence for Self-Organized Clustering (Extended Abstract).
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Interactive Machine Learning Tool for Clustering in Visual Analytics.
Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

2019
Analyzing the Fine Structure of Distributions.
CoRR, 2019

Knowledge Discovery in Quarterly Financial Data of Stocks Based on the Prime Standard using a Hybrid of a Swarm with SOM.
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.
Springer Vieweg, ISBN: 978-3-658-20539-3, 2018

2017
Credible visualizations for planar projections.
Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, 2017


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