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ORCIDAlfred Ultsch
Person information
- affiliation: Philipps University of Marburg, Germany
- affiliation: ETH Zurich, Switzerland
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Books and Theses
- 1987
[b1]Alfred Ultsch:
Control for knowledge based information retrieval. ETH Zurich, Zürich, Switzerland, 1987, ISBN 978-3-7281-1629-1, pp. 1-146
Journal Articles
- 2022
- [j18]Alfred Ultsch, Jörn Lötsch
:
Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans). BMC Bioinform. 23(1): 233 (2022) - 2021
- [j17]Michael C. Thrun, Alfred Ultsch:
Swarm intelligence for self-organized clustering. Artif. Intell. 290: 103237 (2021) - [j16]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) - [j15]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) - [j14]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) - [j13]Michael C. Thrun, Alfred Ultsch, Lutz Breuer:
Explainable AI Framework for Multivariate Hydrochemical Time Series. Mach. Learn. Knowl. Extr. 3(1): 170-204 (2021) - 2020
- [j12]Alfred Ultsch, Jörn Lötsch:
The Fundamental Clustering and Projection Suite (FCPS): A Dataset Collection to Test the Performance of Clustering and Data Projection Algorithms. Data 5(1): 13 (2020) - 2019
- [j11]Catharina Lippmann, Alfred Ultsch, Jörn Lötsch:
Computational functional genomics-based reduction of disease-related gene sets to their key components. Bioinform. 35(14): 2362-2370 (2019) - 2017
- [j10]Alfred Ultsch, Jörn Lötsch:
Machine-learned cluster identification in high-dimensional data. J. Biomed. Informatics 66: 95-104 (2017) - 2013
- [j9]Jörn Lötsch, Alfred Ultsch:
A machine-learned knowledge discovery method for associating complex phenotypes with complex genotypes. Application to pain. J. Biomed. Informatics 46(5): 921-928 (2013) - 2011
- [j8]Fabian Moerchen, Michael Thies, Alfred Ultsch:
Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compression. Knowl. Inf. Syst. 29(1): 55-80 (2011) - 2010
- [j7]Alfred Ultsch, Lutz Herrmann:
Self Organized Swarms for cluster preserving Projections of high-dimensional Data. Electron. Commun. Eur. Assoc. Softw. Sci. Technol. 27 (2010) - 2007
- [j6]Fabian Mörchen, Alfred Ultsch:
Efficient mining of understandable patterns from multivariate interval time series. Data Min. Knowl. Discov. 15(2): 181-215 (2007) - 2006
- [j5]Fabian Mörchen, Alfred Ultsch, Michael Thies, Ingo Lohken:
Modeling timbre distance with temporal statistics from polyphonic music. IEEE Trans. Speech Audio Process. 14(1): 81-90 (2006) - 2005
- [j4]Fabian Mörchen, Alfred Ultsch, Olaf Hoos:
Extracting interpretable muscle activation patterns with time series knowledge mining. Int. J. Knowl. Based Intell. Eng. Syst. 9(3): 197-208 (2005) - 2004
- [j3]Katrin Kupas, Alfred Ultsch, Gerhard Klebe:
Comparison of substructural epitopes in enzyme active sites using self-organizing maps. J. Comput. Aided Mol. Des. 18(11): 697-708 (2004) - 2002
- [j2]Alfred Ultsch, Frank Röske:
Self-organizing feature maps predicting sea levels. Inf. Sci. 144(1-4): 91-125 (2002) - 2001
- [j1]Gabriela Guimarães, Jörg-Hermann Peter, Thomas Penzel, Alfred Ultsch:
A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders. Artif. Intell. Medicine 23(3): 211-237 (2001)
Conference and Workshop Papers
- 2022
- [c55]Michael C. Thrun, Quirin Stier, Alfred Ultsch:
Interactive Toolbox for Two-Dimensional Gaussian Mixture Modeling. ECML/PKDD (6) 2022: 658-661 - 2020
- [c54]Michael C. Thrun, Felix Pape, Alfred Ultsch:
Interactive Machine Learning Tool for Clustering in Visual Analytics. DSAA 2020: 479-487 - [c53]Michael C. Thrun, Alfred Ultsch:
Swarm Intelligence for Self-Organized Clustering (Extended Abstract). IJCAI 2020: 5125-5129 - [c52]Michael C. Thrun, Alfred Ultsch:
Swarm-Based Cluster Analysis for Knowledge Discovery. KI 2020: 240-244 - 2017
- [c51]Alfred Ultsch, Michael C. Thrun:
Credible visualizations for planar projections. WSOM 2017: 77-81 - 2016
- [c50]Alfred Ultsch, Martin Behnisch, Jörn Lötsch:
ESOM Visualizations for Quality Assessment in Clustering. WSOM 2016: 39-48 - 2014
- [c49]Jörn Lötsch, Alfred Ultsch:
Exploiting the Structures of the U-Matrix. WSOM 2014: 249-257 - 2010
- [c48]Martin Behnisch, Alfred Ultsch:
Clustering Temporal Population Patterns in Switzerland (1850-2000). GfKl 2010: 163-171 - 2009
- [c47]Lutz Herrmann, Alfred Ultsch:
Clustering with Swarm Algorithms Compared to Emergent SOM. WSOM 2009: 80-88 - 2008
- [c46]Lutz Herrmann, Alfred Ultsch:
The Architecture of Ant-Based Clustering to Improve Topographic Mapping. ANTS Conference 2008: 379-386 - [c45]Lutz Herrmann, Alfred Ultsch:
Explaining Ant-Based Clustering on the basis of Self-Organizing Maps. ESANN 2008: 215-220 - [c44]Lutz Herrmann, Alfred Ultsch:
Strengths and Weaknesses of Ant Colony Clustering. GfKl 2008: 147-156 - [c43]Florian Meyer, Alfred Ultsch:
Finding Music Fads by Clustering Online Radio Data with Emergent Self Organizing Maps. GfKl 2008: 419-427 - [c42]Alfred Ultsch:
Is Log Ratio a Good Value for Measuring Return in Stock Investments? GfKl 2008: 505-511 - [c41]Martin Behnisch, Alfred Ultsch:
Estimating the Number of Buildings in Germany. GfKl 2008: 585-593 - [c40]Uwe Henker, Uwe Petersohn, Alfred Ultsch:
The Precise and Efficient Identification of Medical Order Forms Using Shape Trees. GfKl 2008: 651-661 - [c39]Alfred Ultsch, Christian Pallasch, Eckhard Bergmann, Holger Christiansen:
A Comparison of Algorithms to Find Differentially Expressed Genes in Microarray Data. GfKl 2008: 685-697 - 2007
- [c38]Lutz Herrmann, Alfred Ultsch:
An Artificial Life Approach for Semi-supervised Learning. GfKl 2007: 139-146 - [c37]Martin Behnisch, Alfred Ultsch:
Urban Data Mining Using Emergent SOM. GfKl 2007: 311-318 - [c36]Pascal Lehwark, Sebastian Risi, Alfred Ultsch:
Visualization and Clustering of Tagged Music Data. GfKl 2007: 673-680 - [c35]Alfred Ultsch:
U*C: Distance and Density Clustering based on Grid Projections. LWA 2007: 81-86 - 2006
- [c34]Mario Nöcker, Fabian Mörchen, Alfred Ultsch:
An algorithm for fast and reliable ESOM learning. ESANN 2006: 131-136 - [c33]Nguyen-Xuan Thinh, Martin Behnisch, Alfred Ultsch:
Examination of Several Results of Different Cluster Analyses with a Separate View to Balancing the Economic and Ecological Performance Potential of Towns and Cities. GfKl 2006: 289-296 - [c32]Fabian Mörchen, Ingo Mierswa, Alfred Ultsch:
Understandable models Of music collections based on exhaustive feature generation with temporal statistics. KDD 2006: 882-891 - 2005
- [c31]Alfred Ultsch, Lutz Herrmann:
The architecture of emergent self-organizing maps to reduce projection errors. ESANN 2005: 1-6 - [c30]Fabian Mörchen, Alfred Ultsch:
Finding Persisting States for Knowledge Discovery in Time Series. GfKl 2005: 278-285 - [c29]David Kämpf, Alfred Ultsch:
An Overview of Artificial Life Approaches for Clustering. GfKl 2005: 486-493 - [c28]Alfred Ultsch:
UnitExpressions: A Rational Normalization Scheme for DNA Microarray Data. GfKl 2005: 678-683 - [c27]Fabian Mörchen, Alfred Ultsch, Mario Nöcker, Christian Stamm:
Visual Mining in Music Collections. GfKl 2005: 724-731 - [c26]Fabian Mörchen, Alfred Ultsch, Mario Nöcker, Christian Stamm:
Databionic Visualization of Music Collections According to Perceptual Distance. ISMIR 2005: 396-403 - [c25]Fabian Mörchen, Alfred Ultsch:
Optimizing time series discretization for knowledge discovery. KDD 2005: 660-665 - [c24]Alfred Ultsch:
U*C: Self-organized Clustering with Emergent Feature Maps. LWA 2005: 240-244 - 2004
- [c23]Alfred Ultsch, David Kämpf:
Knowledge discovery in DNA microarray data of cancer patients with emergent self organizing maps. ESANN 2004: 501-506 - [c22]Alfred Ultsch:
Density Estimation and Visualization for Data Containing Clusters of Unknown Structure. GfKl 2004: 232-239 - [c21]Fabian Mörchen, Alfred Ultsch:
Discovering Temporal Knowledge in Multivariate Time Series. GfKl 2004: 272-279 - [c20]Katrin Kupas, Alfred Ultsch:
Data Mining in Protein Binding Cavities. GfKl 2004: 354-361 - [c19]Alfred Ultsch:
Improving the Identification of Differentially Expressed Genes in cDNA Microarray Experiments. GfKl 2004: 378-385 - [c18]Fabian Mörchen, Alfred Ultsch:
Mining Hierarchical Temporal Patterns in Multivariate Time Series. KI 2004: 127-140 - [c17]Fabian Mörchen, Alfred Ultsch, Olaf Hoos:
Discovering Interpretable Muscle Activation Patterns with the Temporal Data Mining Method. PKDD 2004: 512-514 - 2000
- [c16]Alfred Ultsch:
A Neural Network Learning Relative Distances. IJCNN (5) 2000: 553-558 - 1999
- [c15]Gabriela Guimarães, Alfred Ultsch:
A Method for Temporal Knowledge Conversion. IDA 1999: 369-382 - 1998
- [c14]Alfred Ultsch:
The integration of connectionist models with knowledge-based systems: hybrid systems. SMC 1998: 1530-1535 - 1997
- [c13]Alfred Ultsch, T. O. Kleine, Dieter Korus, S. Farsch, Gabriela Guimarães, W. Pietzuch, J. Simon:
Evaluation of Automatic and Manual Knowledge Acquisition for Cerebrospinal Fluid (CSF) Diagnosis. AIME 1997: 110-121 - 1996
- [c12]Alfred Ultsch, Gabriela Guimarães, Willi Schmid:
Classification and prediction of hail using self-organizing neural networks. ICNN 1996: 1622-1627 - 1995
- [c11]Alfred Ultsch, Dieter Korus, T. O. Kleine:
Integration of Neural Networks and Knowledge-Based Systems in Medicine. AIME 1995: 425-426 - [c10]Alfred Ultsch, Dieter Korus:
Integration of neural networks with knowledge-based systems. ICNN 1995: 1828-1833 - 1994
- [c9]Martin Schweizer, P. M. B. Föhn, Jürg Schweizer, Alfred Ultsch:
A Hybrid Expert System for Avalanche Forecasting. ENTER 1994: 148-153 - 1993
- [c8]Alfred Ultsch, Gabriela Guimarães, Dieter Korus, Heng Li:
Knowledge Extraction from Artificial Neural Networks and Applications. Transputer-Anwender-Treffen 1993: 148-162 - 1992
- [c7]Alfred Ultsch:
Knowledge Acquisition with Self-Organizing Neural Networks. ECAI 1992: 208-210 - [c6]Alfred Ultsch, Gabriela Guimarães, Dieter Korus, Heng Li:
Selbstorganisierende Neuronale Netze auf Transputern. Transputer-Anwender-Treffen 1992: 194-203 - 1991
- [c5]Alfred Ultsch, Günter Halmans, Kira Schulz:
Die Transformation experimenteller Verteilungen durch eine Self-Organizing Feature Map. DAGM-Symposium 1991: 207-214 - [c4]Alfred Ultsch, H. Peter Siemon, Günter Halmans:
Neuronale Netze zur Unterstützung der Umweltforschung. Informatik für den Umweltschutz 1991: 296-302 - [c3]Günther Palm, Ulrich Rückert, Alfred Ultsch:
Wissensverarbeitung in neuronaler Architektur. Wissensbasierte Systeme 1991: 508-518 - 1990
- [c2]Klaus-Uwe Höffgen, H. Peter Siemon, Alfred Ultsch:
Genetic Improvements of Feedforward Nets for Approximating Functions. PPSN 1990: 302-306 - 1987
- [c1]Hans-Jürgen Appelrath, Martin Ester, Heinrich Jasper, Alfred Ultsch:
KOFIS: Ein Expertensystem zur integrierten Dokumenten- und Wissensverwaltung. Expertensysteme 1987: 1-18
Parts in Books or Collections
- 2005
- [p1]Katrin Kupas, Alfred Ultsch, Gerhard Klebe:
An Algorithm for Finding Similarities in protein Active sites. Advances in Bioinformatics and Its Applications 2005
Editorship
- 2013
- [e2]Berthold Lausen, Dirk Van den Poel, Alfred Ultsch:
Algorithms from and for Nature and Life - Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization, Springer 2013, ISBN 978-3-319-00034-3 [contents] - 2010
- [e1]Andreas Fink, Berthold Lausen, Wilfried Seidel, Alfred Ultsch:
Advances in Data Analysis, Data Handling and Business Intelligence - Proceedings of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC), Helmut-Schmidt-University, Hamburg, July 16-18, 2008. Studies in Classification, Data Analysis, and Knowledge Organization, Springer 2010, ISBN 978-3-642-01043-9 [contents]
Informal and Other Publications
- 2021
- [i3]Michael C. Thrun, Alfred Ultsch:
Swarm Intelligence for Self-Organized Clustering. CoRR abs/2106.05521 (2021) - [i2]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) - 2019
- [i1]Michael C. Thrun, Tino Gehlert, Alfred Ultsch:
Analyzing the Fine Structure of Distributions. CoRR abs/1908.06081 (2019)
Coauthor Index
aka: Fabian Moerchen
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