2020
Ultsch, A., Lötsch, J.: The Fundamental Clustering and Projection Suite (FCPS): A data set collection to test the performance of clustering and data projection algorithms, Scientific Data, Vol.5(1), 2020.
Hoffmann, J., Rother, M., Kaiser, U., Thrun, M. C., Wilhelm, C., Gruen, A., Niebergall, U., Meissauer, U., Neubauer, A., Brendel, C.: Determination of CD43 and CD200 surface expression improves accuracy of B-cell lymphoma immunophenotyping, Cytometry Part B: Clinical Cytometry, 2020. (accepted)
Lerch, F., Ultsch, A., Lötsch, J.: Distribution Optimization: An evolutionary algorithm to separate Gaussian mixtures, Sci Reports 2020. (accepted)
Lötsch, J., Ultsch, A.: Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data, International Journal of Molecular Sciences, 2020.
Thrun, M.C.: Improving the Sensitivity of Statistical Testing for Clusterability with Mirrored-Density Plot, in Archambault, D., Nabney, I. & Peltonen, J. (eds.), Machine Learning Methods in Visualisation for Big Data, The Eurographics Association, Norrköping , Sweden, 2020.
Thrun, M.C., Ultsch, A.: Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems, Data in Brief, Vol. 30(C), 2020.
Thrun, M.C., Ultsch, A.: Swarm Intelligence for Self-Organized Clustering, Journal of Artificial Intelligence, 2020. (in press)
Thrun, M.C., Ultsch, A.: Swarm Intelligence for Self-Organized Clustering (Extended Abstract) in Helmert, M. (Ed.), 29th International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, 2020. (accepted)
Thrun, M.C., Ultsch, A.: Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data, Journal of Classification, Springer, 2020. (accepted)
2019
Ultsch, A., Hoffman, J., Brendel, C.: ESOM Sampling as a Tool for Detection of Needles in the Haystack of Big Data in Medical Diagnostic Technologies, in: Kestler, H.A., Schmid, M., Lausser, L., Fürstberger, A., (eds): Statistical Computing 2019, Ulmer Informatik-Bericht, pp. 2-3, 2019.
Lippmann, C., Ultsch, A., Lötsch, J.: Computational functional genomics-based reduction of disease-related gene sets to their key components, Bioinformatics, Vol 35(14), pp. 2362-2370, 2019.
Lötsch, J., Ultsch, A.: Generative artificial intelligence based algorithm to increase the predictivity of preclinical studies while keeping sample sizes small, in: Kestler, H.A., Schmid, M., Lausser, L., Fürstberger, A., (eds): Statistical Computing 2019, Ulmer Informatik-Bericht, pp. 29-30, 2019.
Lötsch, J., Ultsch, A., Kalso, E.: Data-science based subgroup analysis of persistent pain during three years after breast cancer sugery, European Journal of Anaesthesiology, 2019. (accepted)
2018
Ultsch, A., Maul, C.: Fine structure of thermals in arid climate: Glider-based in flight measurements, Meteorological Panel, Conf. International Scientific and Technical Soaring Organization (OSTIV), Bremen, 2018.
Brendel, C., Mack, E., Frech, M., Neubauer, A., Haferlach, T., Ultsch, A.: Identification of specific cluster of differentiation genes in acute myeloid leukemia by combined Bayesian and ABC analysis, poster, 2018.
Kringel, D., Lippmann, C. , Parnham, M.J., Kalso, E., Ultsch, A., Lötsch, J.: A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes, in press for European Journal of Pain,Wiley,2018.
Lippmann, C., Kringel, D., Ultsch, A., Lötsch, J.: Computational functional genomics-based approaches in analgesic drug discovery and repurposing, Pharmacogenomics, Vol. 19, No. 9, pp. 783-797, 2018.
Lötsch, J., Ultsch, A.: Machine learning in pain research, Pain, Vol. 159, pp. 623-630, 2018.
Lötsch, J., Geisslinger, G., Heinemann, S., Lerch, F., Oertel, B.G., Ultsch, A.: Quantitative sensory testing response patterns to capsaicin- and ultraviolet-B-induced local skin hypersensitization in healthy subjects: a machine-learned analysis, Pain, Vol. 159, pp. 11-24, 2018.
Lötsch, J., Lerch, F., Djaldetti, R., Tegeder, I., Ultsch, A.: Idendtification of disease-distinct complex biomarker patterns by means of unsupervised machine-learning using an interactive R toolbox (Umatrix), BMC Big Data Analytics, pp. 1-17, 2018.
Lötsch, J., Schiffmann, S., Schmitz, K., Brunkhorst, R., Lerch, F., Ferreiros, N., Wicker, S., Tegeder, I., Geisslinger, G., Ultsch, A.: Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy, Scientific Reports, Vol. 8 (1), 2018.
Lötsch, J., Sipilä, R., Tasmuth, T., Kringel, D., Estlander, A.M., Meretoja, T., Kalso, E., Ultsch, A.: Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy, Breast Cancer Res Treat, Vol.171(2), pp. 399-411, 2018.
Mack, E.K.M., Marquardt, A., Langer, D., Ross, P., Ultsch, A., Kiehl, M.G., Mack, H.I.D., Haferlach, T., Neubauer. A., Brendel, C.: Comprehensive genetic diagnosis of acute myeloid leukemia by next generation sequencing, Haematologica, ahead of print, 2018.
Mascus, E., Sistach, M.U., Soler, M.R., Ultsch, A., Maul, C.: Exploring gravity waves in the Pyrenees by ground based observations, in-flight measurements, and model analysis, Meteorological Panel, Conf. International Scientific and Technical Soaring Organization (OSTIV), Bremen, 2018.
Thrun, M. C., Ultsch, A.: Effects of the payout system of income taxes to municipalities in Germany, in Papież, M. & Śmiech, S. (eds.), Proc. 12th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena, pp. 533-542, Cracow: Foundation of the Cracow University of Economics, Cracow, Poland, 2018.
Thrun, M. C., Ultsch, A.: Investigating Quality measurements of projections for the Evaluation of Distance and Density-based Structures of High-Dimensional Data, Proc. European Conference on Data Analysis (ECDA), pp. 45-46, Paderborn, Germany, 2018.
Thrun, M. C., Breuer, L., Ultsch, A.: Knowledge discovery from low-frequency stream nitrate concentrations: hydrology and biology contributions, Proc. European Conference on Data Analysis (ECDA), pp. 46-47, Paderborn, Germany, 2018.
Thrun, M. C., Pape, F., Ultsch, A.: Benchmarking Cluster Analysis Methods using PDE-Optimized Violin Plots, Proc. European Conference on Data Analysis (ECDA) pp. 26, Paderborn, Germany, 2018.
2017
Ultsch, A., Behnisch, M.: Effects of the payout system of income taxes to municipalities in Germany, Applied Geography, Vol. 81, pp. 21-31, 2017.
Ultsch, A., Lötsch, J.: Machine-learned cluster identification in high-dimensional data, Journal of Biomedical Informatics, Vol. 66, pp. 95-104, 2017.
Ultsch, A., Lötsch, J.: Generative learning with emergent self-organizing neuronal networks, accepted for publication at Conf. Int. Federation of Classification Societies, Tokyo, 2017.
Ultsch, A., Thrun, M.: Credible Visualizations for Planar Projections, Proc. Workshop on Self-Organizing Maps (WSOM), pp. 256-260, Nancy, 2017.
Lötsch, J., Ultsch, A.: Random forests followed by ABC analysis as a feature selection procedure for machine‐learning, Conf. Int. Federation of Classification Societies, Tokyo, 2017.
Lötsch, J., Ultsch, A.: A data science based standardized Gini index as a Lorenz dominance preserving measure of the inequality of distributions, PLOS ONE, Vol. 12(8), August, 2017.
Lötsch, J., Ultsch, A., Kalso, E.: Prediction of persistent post-surgery pain by preoperative cold pain sensitivity: Biomarker development with machine-learning-derived analysis, British Journal of Anaesthesia, Vol. 119, pp. 821-829, 2017.
Lötsch, J., Lippmann, C., Kringel, D., Ultsch, A.: Integrated computational analysis of genes associated with human hereditary intensitivity to pain. A drug repurposing perspective, Frontiers in Molecular Neuroscience, August, 2017.
Lötsch, J., Sipila, R., Tasmuth, T., Kringel, D., Estlander, A.-M., Meretoja, T., Kalso, E., Ultsch, A.: A machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy, under review for Pain, 2017.
Lötsch, J., Thrun, M., Lerch, F., Brunkhorst, R., Schiffmann, S., Thomas, D., Tegder, I., Geisslinger, G., Ultsch, A.: Machine-learned data structures of lipid marker serum concentrations in multiple sclerosis patients differ from those in healthy subjects, Int. J. Mol. Sci., Vol. 18(6), 2017.
Thrun, M., Ultsch, A.: Projection based Clustering, Conf. Int. Federation of Classification Societies, Tokyo, 2017.
2016
Ultsch, A., Behnisch, M., Lötsch, J.: ESOM Visualizations for Quality Assessment in Clustering, In: Merényi, E., Mendenhall, J. M., O’Driscoll, P., (Eds.): Advances in Self-Organizing Maps and Learning Vector Quantization, Proc. WSOM, Houston, Texas, USA, pp. 39-48, Springer, New York, 2016.
Ultsch, A., Curtius, J., Maul, C.: Data Mining for Atmospheric Gravity Waves (Lee Waves), Technical Soaring, Vol. 40, No. 3, 2016.
Ultsch, A., Kringel, D., Kalso, E., Mogil, J. S., Lötsch, J.: A data science approach to candidate gene selection of pain regarded as a process of learning and neural plasticity, Pain, Vol. 157, pp. 2747-2757, 2016.
Aubert, A. H., Thrun, M. C., Breuer, L., Ultsch, A.: Knowledge discovery from high-frequency stream nitrate concentrations: hydrology and biology contributions, Scientific Reports, Nature, Vol. 6(31536), pp. 1-8, 2016.
Knothe, C., Doehring, A., Ultsch, A., Lötsch, J.: Methadone induces hypermethylation of human DNA, Epigenomics, Vol. 8(2), pp. 167-179, 2016.
Knothe, C., Oertel, B.G., Ultsch, A., Kettner, M., Schmidt, P.H., Wunder, C., Toennes, S.W., Geisslinger, G., Lötsch, J.: Pharmacoepigenetics of the role of DNA methylation in µ-opioid receptor expression in different human brain regions, Epigenomics, 2016.
Knothe, C., Shiratori, H., Resch, E., Ultsch, A., Geisslinger, G., Doehring, A., Lötsch, J.: Disagreement between two common biomarkers of global DNA methylation, Clinical Epigenetics, Vol. 8:60, pp. 1-17, 2016.
Kringel, D., Ultsch, A., et al. : Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses, The Pharmacogenomics Journal, 5, pp. 1-8, 2016.
Lötsch, J., Ultsch, A. : A machine-learned computational functional-genomics based approach to drug classification, European Journal of Clinical Pharmacology, Volume 72, Issue 12, pp. 1449-1461, 2016.
Lötsch, J., Ultsch, A.: A computational functional genomics based self-limiting self-concentration mechanism of cell specialization as a biological role of jumping genes, Integrative Biology, The Royal Society of Chemistry, Vol. 8(1), pp. 91-103, 2016.
Lötsch, J., Ultsch, A.: Process Pharmacology: A Pharmacological Data Science Approach to Drug Development and Therapy, Pharmacometrics & Systems Pharmacology, Vol. 5(4), pp. 192-200, 2016.
Lötsch, J., Ultsch, A.: Process pharmacology: Using computational functional genomics knowledge to connect drugs with biological processes, In: Fürstenberg, et al., H.A., (Eds), Proc. Statistical Computing, Universty of Ulm, p. 3, Ulm, 2016.
Lötsch, J., Ultsch, A., Eckhard, M., Huart, C., Rombaux, P., Hummel, T.: Brain lesion-pattern analysis in patients with olfactory dysfunctions following head trauma, Neuroimage Clin., Vol. 11, pp. 99-105, 2016.
Lötsch, J., Ultsch, A., Hummel, T.: A unifying data driven model of human olfactory pathology representing known etiologies of dysfunction, Chem Senses, Vol. 00, pp. 1-8, 2016.
Lötsch, J., Ultsch, A., Hummel, T.: How Many and Which Odor Identification Items Are Needed to Establish Normal Olfactory Function?, Chemical Senses, Vol. 41(4), pp. 339-344, 2016.
Lötsch, J., Dimova, V., Ultsch, A., et al. : A small yet comprehensive subset of human experimental pain models emerging from correlation analysis with a clinical quantitative sensory testing protocol in healthy subjects, European Journal of Pain, Vol. 20, pp. 777-789, 2016.
Lötsch, J., Hähner, A., Gorau, G., Hummel, C., Walter, C., Ultsch, A., Hummel, T.: The smell of pain: intersection of nociception and olfaction, Pain 5, 2016.
Lötsch, J., Hummel, T., Ultsch, A.: Machine-learned pattern identification in olfactory subtest results, Nature Scientific Reports, October, pp. 1-8, 2016.
Mack, E., Langer, D., Marquardt, A., Ultsch, A., Kiehl, M. G., Neubauer, A., Brendel C. A.: Comprehensive Genetic Diagnostics of Acute Myeloid Leukemia By Next Generation Sequencing, Proceeding of 58th Annual Meeting & Exposition, San Diegeo, CA, 2016.
Thrun, M. C., Lerch, F., Lötsch, J., Ultsch, A.: Visualization and 3D Printing of Multivariate Data of Biomarkers, In: Skala, V. (Ed.): Conf. on Computer Graphics, Visualization and Computer Vision, Plzen, pp. 1-384, 2016.
2015
Ultsch, A., Lötsch, J.: Computed ABC analysis for rational selection of most informative variables in multivariate data, PloS one, Vol. 10(6), pp. 1-15, 2015.
Ultsch, A., Kretschmer, O., Behnisch, M.: Systematic Data-Mining into Land Consumption in Germany, in: Ferreira Jr., J., Goodspeed, R. (eds.), Planning Support Systems and Smart Cities, Cambridge, MA, pp. 301-1 – 301-19, 2015.
Ultsch, A., McGrath, A.: Clustering and Classification of Infrared Hyperspectral Aerial images, European Conference on Data Analysis, p. 37, Colchester, 2015.
Ultsch, A., Rogos, C., Maul, C.: Data Mining in Atmospheric Gravity Waves, European Conference on Data Analysis, p. 108, Colchester, 2015.
Ultsch, A., Schnabel, S., Thrun, M. C.: Models of Income Distributions for Knowledge Discovery, European Conference on Data Analysis, pp. 136-137, Colchester, 2015.
Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lötsch, J.: Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox(AdaptGauss), International Journal of Molecular Sciences, Vol. 16, pp. 25897-25911, 2015.
Ultsch, A., Weingart, M., Lötsch, J.: 3-D printing as a tool for knowledge discovery in high dimensional data spaces, in: Kestler, H.A., Schmid, M., Kraus, J.M., Lausser, L., Fürstberger, A. (eds): Statistical Computing 2015, Ulmer Informatik-Berichte, pp. 12, 2015.
Behnisch, M., Ultsch, A.: Knowledge Discovery in Spatial Planning Data – A Concept for Cluster Understanding, in: Helbich, M., Arsanjani, J. J., Leitner, M. (eds.): Computational Approaches for Urban Environments, in: Gatrell, J.D., Jensen, R.R.: Geotechnologies and the Environment Series, Vol, 13, Springer, Berlin, pp. 49-75, 2015.
Behnisch, M., Ultsch, A.: Does Landscape Attractiveness affect Land Consumption in Germany?, European Conference on Data Analysis, pp. 109-110, Colchester, 2015.
Behnisch, M., Kretschmer, O., Schwarzak, M., Ultsch, A.: Towards an Understanding of Land Consumption in Germany– Outline of Influential Factors as a Basis for Multidimensional Analyses, in: Erdkunde – Archive for Scientific Geography, Vol. 69 (3), pp. 267-279, 2015.
Dimova, V., Oertel, B.G., Kabakci, G., Zimmermann, M., Hermens, H., Lautenbacher, S., Ultsch, A., Lötsch, J.: A more pessimistic life-orientation is associated with experimental inducibility of neuropathy-like pain pattern in healthy subjects, J Pain, pp. 791-800, 2015.
Knothe, C., Doehring, A., Ultsch, A., Lötsch.: Methadone induces hypermethylation of human DNA, 2015.
Kringel, D., Ultsch, A., Zimmermann, M., Jansen, JP., Ilias, W., Freynhagen, R., Griessinger, N., Kopf, A., Stein, C., Doehring, A., Resch, E., Lötsch, J.: Emergent biomarker derived from next generation sequencing to identify pain patients requiring uncommonly high opioid doses, Pharmacogenomics J, 2015.
Lippmann, C., Lötsch, J., Ultsch, A.: Understanding the Biological Functions of Gene Sets, European Conference on Data Analysis, pp. 28-29, Colchester, 2015.
Lötsch, J., Dimova, V., Hermens, H., Zimmermann, M., Geisslinger, G., Oertel, BG., Ultsch, A.: Pattern of neuropathic pain induced by topical capsaicin application in healthy subjects, Pain, Vol. 3, pp. 405-414, 2015.
Lötsch, J., Ultsch, A.: New Methods for the Classification of inequally distributed Data: ABC-plots and computed ABC-analysis, European Conference on Data Analysis, pp. 121-122, Colchester, 2015.
Lötsch, J., Ultsch, A.: A computational functional genomics based self-limiting self-concentration mechanism of cell specialization as a biological role of jumping genes, Integrative Biology, Vol. 8(1), pp. 91-103, 2015.
Lötsch, J., Niederberger, E., Ultsch, A.: Computational functional genomics based analysis of pain-relevant micro-RNAs, Hum Genet, Vol. 134, pp. 1221-1238, 2015.
Lötsch, J., Daiker, H., Hähner, A., Ultsch, A., Hummel, T.: Drug-target based cross-sectional analysis of olfactory drug effects, Eur J Clin Pharmacol 2015.
Lötsch, J., Dimova, V., Lieb, I., Zimmermann, M., Oertel, BG., Ultsch, A.: Multimodal distribution of human cold pain thresholds, PLoS One 2015.
Lötsch, J., Dimova, V., Ultsch, A., Lieb, I., Zimmermann, M., Geisslinger, G., Oertel, BG.: A small yet comprehensive subset of human experimental pain models emerging from correlation analysis with a clinical quantitative sensory testing protocol in healthy subjects, Pain, 156, pp. 405-414, 2015.
Lötsch, J., Knothe, C., Lippmann, C., Ultsch, A., Hummel, T., Walter, C.: Olfactory drug effects approached from human-derived data, Drug Discov Today 2015.
Lötsch, J., Reither, N., Bogdanov, V., Hähner, A., Ultsch, A., Hill, K., Hummel, T.: A brain-lesion pattern based algorithm for the diagnosis of post-traumatic olfactory loss, Rhinology 53, pp.365-370, 2015.
2014
Ultsch, A.: Datenbionik: Selbstorganisierende Systeme zur Entdeckung ungewöhnlicher Strukturen in Unternehmensdaten, in: Herde, G.(ed): Transparenz durch digitale Datenanalyse, Erich Schmidt Verlag, pp. 37-52, 2014 (ISBN 978 3 503 15675 7).
Ultsch, A., Lötsch, J.: Functional abstraction as a method to discover knowledge in gene ontologies. in: Schönbach. C., PLoS One, 9(2), pp. 90-191, 2014.
Ultsch, A., Lötsch, J.: What do all the (human) micro-RNAs do?, A functional genomics perspective, DOI: 10.1186/1471-2164-15-976, BMC Genomics, 2014.
Ultsch, A., Pallasch, C., Herda, S., Lötsch, J.: What do all those miRNAs do?, in: Kestler, HA., Schmid, M., Lausser, L., Kraus, JM. (eds): Statistical Computing 2014, Ulmer Informatik-Berichte, pp. 16, 2014 (ISSN 0939-5091).
Lötsch, J., Ultsch, A.: Exploiting the structures of the U-matrix, in Villmann, T., Schleif, F.-M., Kaden, M. & Lange, M. (eds.), Proc. Advances in Self-Organizing Maps and Learning Vector Quantization, pp. 249-257, Springer International Publishing, Mittweida, Germany, 2014.
Lötsch, J., Oertel, B. G., Ultsch, A.: Human models of pain for the prediction of clinical analgesia, Pain, Vol. 155(10), pp. 2014-21, 2014, (ISSN 0304-3959).
Schmitz, K., deBruin, N., Bishay, P., Männich, J., Häussler, A., Ferreirós, N., Lötsch, J., Ultsch, A., Parnham, M.J., Geisslinger, G., Tegeder, I.: R-flurbiprofen attenuates experimental autoimmune encephalomyelitis in mice, EMBO Mol Med, 2014.
Walter, C., Oertel, B. G., Ludyga, D., Ultsch, A., Hummel, T., Lötsch, J.: Effects of 20 mg oral Delta-9-tetrahydrocannabinol on human olfaction, British Journal of Clinical Pharmacology, Vol, 78, pp. 961-969, 2014.
2013
Ultsch, A.: Swarm Data Mining for the Fine Structure of Thermals, in: Technical Soaring, Vol. 36, Nr. 4, pp. 37 – 44, 2013.
Lausen, B., Van den Poel, D., Ultsch, A.(eds): Algorithms from and for Nature and Life – Classification and Data Analysis, Studies in Classification, Data Analysis and Knowledge Organization, Springer, New York, 2013 (ISBN 978 3 319 00034 3).
Lötsch, J., Doehring, A., Mogil, J.S., Arndt, T., Geisslinger, G., Ultsch, A.: Functional genomics of pain in analgesic drug development and therapy, in: Pharmacology & Therapeutics, Volume 139, Issue 1, pp. 60–70, 2013.
Lötsch, J., Schaeffeler, E., Mittelbronn, M., Winter, S., Gudziol, V., Schwarzacher, S.W., Hummel, T., Doehring, A., Schwab, M., Ultsch, A.: Functional genomics suggest neurogenesis in the adult human olfactory bulb, Brain Structure & Function, Springer, Berlin, 2013.
Lötsch, J., Ultsch, A.: A machine-learned knowledge discovery method for associating complex phenotypes with complex genotypes, Application to pain, Journal of biomedical informatics, Vol. 46(5), pp. 921-928. 2013.
Lötsch, J., Skarke, C., Darimont, J., Zimmermann, M., Bräutigam, L., Geisslinger, G., Ultsch, A., Oertel, BG.: Non-invasive combined surrogates of remifentanil blood concentrations with relevance to analgesia, accepted for Naunyn-Schmiedeberg’s, Archives of Pharmacology, Volume 386, Issue 10, pp. 865-873, 2013.
2012
Behnisch, M., Ultsch, A.: Gibt es gemeinsame Muster in der Populationsentwicklung von Schweizer Gemeinden?
in: Thinh, N. X., Behnisch, M., Margraf, O. (eds): Beiträge zur Theorie und quantitativen Methodik in der Geographie, in: Rhombos-Verlag, Berlin, pp. 37-54, 2012 (ISBN 978-3-941216-67-9).
Lötsch, J., Ultsch, A.: Functional abstraction for large Gene Ontology taxonomies, in: Kestler, HA., Binder, H., Schmid, M., Kraus, JM. (eds): Statistical Computing 2012, Ulmer Informatik-Berichte, pp. 6, 2012 (ISSN 0939-5091).
Schlecker, C., Ultsch, A., Geisslinger, G., Lötsch, J.: The pharmacogenetic background of hepatitis C treatment, in: Mutation Research, Volume 751, pp. 36-48, 2012.
2011
Moerchen, F., Thies, M., Ultsch, A.: Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compression, Knowl. Inf. Syst, 29(1), pp. 55-80, 2011.
Stegemann, B., Klebe, G.: Cofactor-binding sites in proteins of deviating sequence: Comperative analysis and clustering in torsion angle, cavity and fold space, in: Proteins, Volume 80, pp. 626-648, 2011.
Lötsch, J., Hofmann, W.P, Schlecker, C., Zeuzem, S., Geisslinger, G., Ultsch, A., Doehring, A.: Single and combined IL28B, ITPA and SLC28A3 host genetic markers modulating response to anti-hepatitis C therapy: in: pharmazentrum frankfurt/ZAFES, Institute for Clinical Pharmacology, Goethe University, Volume 12, Nr. 12, pp. 1729-1740, 2011.
Lötsch, J., Ultsch, A.: Association of complex human pain phenotypes with complex pain genotypes using a self-organizing maps approach. Joint Conference of the German Classification Society (GfKl) and the German Association for Pattern Recognition (DAGM): Algorithms from & for Nature and Life. Frankfurt am Main, Germany. pp. 120, 2011.
2010
Ultsch, A.: Is log ratio a good Value for measuring Return in Stock Investments?, in: Fink, A. et al (Eds.) Advances in Data Analysis. Data Handling and Business Intelligence, Springer Studies in Classification, Data Analysis and Knowledge Organization, Springer, Heidelberg, pp. 505-511, 2010.
Ultsch, A., Herrmann,L.: Self Organized Swarms for cluster preserving Projections of high-dimensional Data, Workshop über Selbstorganisierende, adaptive kontextsensitive verteilte Systeme,Electronic Communications of the EASST Volume 27, 2010.
Behnisch, M., Ultsch, A.: Urban Data Mining – Eine Methodik zur raumbezogenen Wissensextraktion, in: GIS Science Zeitschrift für Geoinformatik, pp. 135-147, 2010.
Behnisch, M., Ultsch, A.: Clustering Temporal Population Patterns in Switzerland 1850-2000 in: Gaul, W. et al (Eds.) Advances in Data Analysis, Data Handling and Business Intelligence (Proc. of the 34nd Annual Conference of the Gesellschaft für Klassifikation.e.V., Karlsruhe), Springer, Heidelberg, pp. 163-173, 2010.
Heise, R., Ultsch, A.: Data Mining to Distinguish Wave from Thermal Climbs in Flight Data, in: Gaul, W. et al (Eds.) Advances in Data Analysis, Data Handling and Business Intelligence (Proc. of the 34nd Annual Conference of the Gesellschaft für Klassifikation e.V., Karlsruhe), Springer, 2010.
2009
Ultsch, A.: The U-Matrix as Visualization for Projections of high-dimensional data, in: Locarek-Junge, H. et al. (Eds.) Classification as a Tool for Research, Proc. 11th IFCS Biennial Conference, 2009.
Ultsch, A., Locarek-Junge, H.: Knowledge Discovery in Stock Market Data, in: Locarek-Junge, H. et al. (Eds.) Classification as a Tool for Research, Proc. 11th IFCS Biennial Conference, 2009.
Behnisch, M., Ultsch, A.: Estimating the number of buildings in Germany, in: Andreas Fink, Berthold Lausen, Wilfried Seidel, Alfred Ultsch (eds.): Advances in Data Analysis, Data Handling and Business Intelligence (Proc. of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Hamburg), pp. 311-318, Springer, Berlin, 2009. http://www.springerlink.com/content/n307l744080488q6/.
Behnisch, M., Ultsch,A.: Are there cluster of communities with the same dynamic behaviour?, in: Hermann Locarek-Junge, Claus Weihs (eds.): Classification as a Tool for Research. (Proc. of the 11th Bi-Annual Conference of the International Federation of Classification Societies, IFCS, Dresden University of Technology, pp. 13-18, 2009.
Behnisch, M., Ultsch, A.: Urban data mining: spatiotemporal exploration of multidimensional data, in: BuildingResearch & Information, Vol. 37, Nr. 5-6, 2009. http://www.informaworld.com/smpp/content~content=a914935599~db=all~jumptype=rss.
Henker, U., Petersohn, U., Ultsch,A.: The Precise and Efficient Identification of Medical Order Forms Using Shape Trees, in: Andreas Fink, Berthold Lausen, Wilfried Seidel, Alfred Ultsch (eds.): Advances in Data Analysis, Data Handling and Business Intelligence, Springer, Studies in Classification, Data Analysis and Knowledge Organization, Springer, pp. 651-661, 2009.http://www.springerlink.com/content/v727133323686123/.
Herrmann, L., Ultsch, A.: Clustering with Swarm Algorithms Compared to Emergent SOM, In Advances in Self-Organizing Maps, 7th International Workshop, WSOM, St. Augustine, Florida, 2009.
Herrmann, L., Ultsch, A.: The Architecture of Ant-Based Clustering to Improve Topographic Mapping. ANTS Conference 2008, pp. 379-386, 2009.
Meyer, F., Ultsch, U.: Finding Music Fads by Clustering Online Radio Data with Emergent Self Organinzing Maps, in: Andreas Fink, Berthold Lausen, Wilfried Seidel, Alfred Ultsch (eds.): Advances in Data Analysis, Data Handling and Business Intelligence (Proc. of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Hamburg) pp. 419-427, 2009
Ohrndorf, P.: Die Identifikation von Leewellen mit Hilfe von Flugwegaufzeichnungen am Beispiel ausgewählter Segelflüge im Alpenraum, Thesis, Departement of Geography, University Marburg, 2009.
2008
Ultsch, A.: Is Log Ratio a Good Value for Measuring Return in Stock Investments? GfKl 2008, pp, 505-511, 2008.
Ultsch, A., Pallasch, C., Bergmann, E., Christiansen, E.: A Comparison of Algorithms to find Differentially Expressed Genes in Microarray Data, in: Proceedings 32nd Annual Conference of the German Classification Society GfKl 2008, Hamburg, Germany, 2008 http://www.springerlink.com/content/k605853l16464748.
Behnisch, M., Ultsch, A.: Estimating the Number of Buildings in Germany, in: C. Preisach et al. (Eds)., Data Analysis, Machine Learning and Applications, Springer, pp, 311-318, 2008.
Fink, A., Lausen, B., Seidel, W., Ultsch, A.: (eds.) /:/Advances in Data Analysis, Data Handling and Business Intelligence, Studies in Classification, Data Analysis, and Knowledge Organization, Springer Berlin, 2008, http://ww.springer.com/series/1564
Henker, U., Petersohn, U., Ultsch, A.: The precise and efficient identification of medical order forms using Shape Trees, to appear in: Proceedings 32nd Annual Conference of the German Classification Society GfKl 2008, Hamburg Germany, 2008.
Herrmann, L., Ultsch, A.:Explaining Ant-Based Clustering on the basis of Self-Organizing Maps, Verleysen M. (Eds), In Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2008), Bruges, pp, 215 – 220, Belgium, 2008, http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2008-51.pdf.
Herrmann, L., Ultsch, A.: The Architecture of Ant-Based Clustering to Improve Topographic Mapping, Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A. (Eds.), in: Ant Colony Optimization and Swarm Intelligence – Proceedings 6th Int. Conf. (ANTS 2008), Brussels, Springer-Verlag Berlin Heidelberg, pp, 379-386, Belgium, 2008.
Herrmann, L., Ultsch, A.: Strengths and Weaknesses of Ant Colony Clustering, in: Proceedings 32nd Annual Conference of the German Classification Society GfKl 2008, Hamburg, Germany, 2008 http://www.springerlink.com/content/q3440032m718r645.
Lehwark, P., Risi, S., Ultsch, U.: Visualization and Clustering of Tagged Music Data, in: Data Analysis, Machine Learning and Applications, Studies in Classification, Data Analysis, and Knowledge Organization, Springer, pp, 673-680, 2008.
Meyer, F., Ultsch, A.: Finding Music Fads by clustering Online Radio Data whith Emergent Self Organizing Maps, to appear in: Proceedings 32nd Annual Conference of the German Classification Society, GfKl 2008, Hamburg, Germany, 2008.
Pallasch, C. P., Schulz, A., Kutsch, N., Schwamb, J., Hagist, S., Kashkar, H., Ultsch, A., Wickenhauser, C., Hallek, M., Wendtner, C.-M.: Overexpression of TOSO in CLL is triggered by B-cell receptor signaling and associated with progressive disease, Blood. 2008, 18708628 (P,S,E,B,D).
2007
Ultsch, A.: Emergence in Self-Organizing Feature Maps, in Ritter, H., Haschke, R.: Proceedings 6th Int. Workshop on Self-Organizing Maps, WSOM ’07, Bielefeld, Germany, 2007.
Ultsch, A.: Using Information Retrieval Methods for a Comparison of Algorithms to find differentially expressed Genes in Microarray Data, Technischer Report, Fachbereich Mathematik und Informatik, Uni Marburg, 2007.
Ultsch, A.: U*C: Distance and Density Clustering based on Grid Projections. LWA 2007, pp, 81-86, 2007.
Behnisch, M., Ultsch, A.: Urban Data Mining using Emergent SOM. in: Preisach, C.; Burkhardt, H.; Schmidt-Thieme, L; Decker, R. (eds.): Data Analysis, Machine Learning and Applications, (Proc. of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7–9, 2007), pp, 311-318, Springer Verlag, Berlin, 2007.http://www.springerlink.com/content/g3v253xu52g52k54/.
Herrmann, L., Ultsch, A.: An Artificial Life Approach for Semi-Supervised Learning, in: Proceedings 31st Annual Conference of the German Classification Society, GfKl 2007, Freiburg, Germany, 2007.
Herrmann, L., Ultsch, A.: Label Propagation for Semi-Supervised Learning in Self-Organizing Maps, in: Proceedings 6th Int. Workshop on Self-Organizing Maps, WSOM ’07, Bielefeld, Germany, 2007.
Kupas, K., Ultsch, A., Klebe, G.: Large scale analysis of protein-binding cavities using self-organizing maps and wavelet-based surface patches to describe functional properties, selectivity discrimination, and putative cross-reactivity. Proteins. 2007 N: 18041748 (P,S,E,B,D).
Lehwark, P., Risi, S., Ultsch, A.: Visualization and clustering of tagged music data. in: Proceedings 31st Annual Conference of the German Classification Society, GfKl 2007, Freiburg, Germany, 2007.
Mörchen, F., Ultsch, A.: Efficient mining of understandable patterns from multivariate interval time series, Data Mining and Knowledge Discovery, Springer Netherlands, 2007 ISSN 1384-5810 (Print), pp, 1573-756X (Online).
Pallasch, C. P., Schwamb, J., Königs, S., Schulz, A., Debey, S., Kofler, D., Schultze, J. L., Hallek, M., Ultsch, A., Wendtner, C. M.: Targeting lipid metabolism by the lipoprotein lipase inhibitor orlistat results, in: apoptosis of B-cell chronic lymphocytic leukemia cells. Leukemia. 2007, 18079738 (P,S,E,B,D).
Risi, S., Mörchen, F., Ultsch, A., Lewark, P.: Visual mining in music collections with Emergent SOM, in: Proceedings Workshop on Self-Organizing Maps (WSOM ’07), Bielefeld, Germany, 2007, ISBN: 978-3-00-022473-7.
2006
Ultsch, A.: Analysis and practical results of U*C clustering, in: Proceedings 30th Annual Conference of the German Classification Society, GfKl 2006, Berlin, Germany, 2006.
Ultsch, A., Herrmann, L.: Automatic Clustering with U*C, Technical Report, Dept. of Mathematics and Computer Science, Philipps-University of Marburg, 2006.
Ultsch, A., Mörchen, F.: U-Maps: topographic visualization techniques for projections of high dimensional data, in: Proceedings 30th Annual Conference of the German Classification Society GfKl 2006, Berlin, Germany, 2006.
Mörchen, F., Ultsch, A., Hoos, O.: Extracting interpretable muscle activation patterns with Time Series Knowledge Mining, International Journal of Knowledge-Based & Intelligent Engineering Systems 9(3), pp, 197-208, 2006.
Mörchen, F., Ultsch, A., Thies, M., Löhken, I.: Modelling timbre distance with temporal statistics from polyphonic music, IEEE Transactions on Speech and Audio Processing 14(1)IEEE, pp, 81-90, 2006.
Mörchen, F., Mierswa, I.,Ultsch, A.: Understandable models Of music collections based on exhaustive feature generation with temporal statistics. KDD 2006, pp, 882-891, 2006.
Nöcker, M., Mörchen, F., Ultsch, A.: Fast and reliable ESOM learning, Proceedings 14th European Symposium on Artificial Neural Networks, Bruges, Belgium, pp 131-136, 2006.
Thinh, N. X., Behnisch, M., Ultsch, A.: Examination of several results of different cluster analysis with a separate view to balancing the economic and ecological performance potential of towns and cities, in: BOCK, H.H., GAUL, W., VICHI, M. (eds.): Studies in Classification, Data Analysis, and Knowledge Organization. Proceedings, 30. Jahrestagung der Gesellschaft für Klassifikation, pp, 289-296. Springer, Berlin, 2006.
2005
Ultsch, A.: Pareto density estimation: A density estimation for knowledge discovery, in Baier, D.; Werrnecke, K. D., (Eds), Innovations in classification, data science, and information systems, Proc Gfkl 2003, pp 91-100, Springer, Berlin, 2005.
Ultsch, A.: Clustering with SOM: U*C, In Proceedings Workshop on Self-Organizing Maps (WSOM 2005), pp, 75-82, Paris, France, 2005.
Ultsch, A.: Density Estimation and Visualization for Data containing Clusters of unknown Structure., Weihs, C., Gaul, W. (Eds), In Classification; The Ubiquitous Challenge, Proceedings 28th Annual Conference of the German Classification Society, GfKl 2004, Dortmund, Germany, Springer, Heidelberg, pp, 232-239, 2005.
Ultsch, A.: Improving the identification of differentially expressed genes in cDNA microarray experiments, Weihs, C., Gaul, W. (Eds), In Classification; The Ubiquitous Challenge, Proceedings 28th Annual Conference of the German Classification Society, GfKl 2004, Dortmund, Germany, Springer, Heidelberg, pp, 378-385, 2005.
Ultsch, A.: U*C: Self-organizied Clustering with Emergent Feature Map, In Proceedings Lernen, Wissensentdeckung und Adaptivität (LWA/FGML 2005), Saarbrücken, Germany, pp. 240-244, 2005.
Ultsch, A., Herrmann, L.: The architecture of emergent self-organizing maps to reduce projection errors, Verleysen M. (Eds), In Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2005), pp, 1-6, 2005.
Ultsch, A., Mörchen, F.: ESOM-Maps: tools for clustering, visualization, and classification with Emergent SOM, Technical Report No. 46, Dept. of Mathematics and Computer Science, University of Marburg, Germany, 2005.
Ultsch, A., Mörchen, F., Efthymiou, N., Kümmerer, M., Löhken, I., Nöcker, M., Stamm, C., Thies, M.: MusicMiner – Ein datenbionisches System zur Organisation von Musiksammlungen, Kooperationspartner in Forschung und Innovation Wiesbaden, 2005.
Ultsch, A., Moutarde, F.: U*F Clustering: a new performant Cluster-mining method based on segmentation of Self-Organizing Maps, in: Proceedings Workshop on Self-Organizing Maps (WSOM 2005), Paris, France, pp, 25-32, 2005.
Koch, O., Kupas, K., Ultsch, A., Klebe, G.: Involvement of turns in ligand binding: Using Secbase to analyse secondary structure elements, Poster German Conference on Bioinformatics (GCB 2005), 2005.
Kupas, K., Ultsch, A.: Data Mining in Protein Binding Cavities, Weihs, C., Gaul, W. (Eds), in: Classification – the Ubiquitous Challenge, Proceedings 28th Annual Conference of the German Classification Society (GfKl 2004), Dortmund, Germany, Springer, Heidelberg, pp, 354-361, 2005.
Mörchen, F., Ultsch, A.: Discovering Temporal Knowledge in Multivariate Time Series, Weihs, C., Gaul, W. (Eds), in: Classification; The Ubiquitous Challenge, Proceedings 28th Annual Conference of the German Classification Society (GfKl 2004), Dortmund, Germany, Springer, Heidelberg, pp, 272-279, 2005.
Mörchen, F., Ultsch, A.: Finding persisting states for knowledge discovery in time series, in: From Data and Information Analysis to Knowledge Engineering – Proceedings 29th Annual Conference of the German Classification Society (GfKl 2005), Magdeburg, Germany, Springer, Heidelberg, pp, 278-285, 2005.
Mörchen, F., Ultsch, A.: Optimizing Time Series Discretization for Knowledge Discovery, Grossman, R.L., Bayardo, R., Bennet, K., Vaidya, J. (Eds), In Proceedings The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, pp, 660-665, 2005.
Mörchen, F., Ultsch, A., Nöcker, M., Stamm, C.: Databionic visualization of music collections according to perceptual distance, Joshua D. Reiss, Geraint A. Wiggins (Eds), In Proceedings 6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK, pp, 396-403, 2005.
Mörchen, F., Ultsch, A., Nöcker, M., Stamm, C.: Visual mining in music collections, in: Proceedings 29th Annual Conference of the German Classification Society (GfKl 2005), Magdeburg, Germany, Springer, Heidelberg, 2005, (to appear).
Mörchen, F., Ultsch, A., Thies, M., Löhken, I. and Nöcker, M., Stamm, C., Efthymiou, N., Kümmerer, M.: MusicMiner: Visualizing timbre distances of music as topographical maps, Technical Report No. 47, Dept. of Mathematics and Computer Science, University of Marburg, Germany, 2005.
2004
Ultsch, A.: Strategies for an Artificial Life System to cluster high dimensional Data, Ulrike Brüggemann, Harald Schaub, Frank Detje (Eds), In Abstracting and Synthesizing the Principles of Living Systems, GWAL-6, Bamberg, pp, 128-137, 2004.
Ultsch, A., Kämpf, D.: Knowledge Discovery in DNA Microarray Data of Cancer Patients with Emergent Self Organizing Maps, In Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2004), pp, 501-506, 2004.
| Kupas, K., Klebe, G., Ultsch, A.: Comparison of substructural epitopes in enzyme active sites using self-organizing maps, Journal of Computer-Aided Molecular Design 18, pp, 697-708, 2004. |
| Kupas, K., Klebe, G., Ultsch, A.: An algorithm for finding similarities in protein active sites, Matthew He, Giri Narasimhan, Sergei Petoukhov (Eds), In Advances in Bioinformatics and its Applications, Proceedings of the International Conference, Nova Southeastern University, Fort Lauderdale, Florida, USA, World Scientific, pp, 373-380, 2004. |
| Mörchen, F., Ultsch, A.: Mining Hierarchical Temporal Patterns in Multivariate Time Series, Susanne Biundo, Thom W. Frühwirth, Günther Palm (Eds), In KI 2004: Advances in Artificial Intelligence, Proceedings 27th Annual German Conference in AI, Ulm, Germany, Springer, Heidelberg, pp, 127-140, 2004. |
| Mörchen, F., Ultsch, A., Hoos, O.: Discovering interpretable muscle activation patterns with the Temporal Data Mining Method, Jean-Francois Boulicaut, Floriana Esposito, Fosca Giannotti and Dino Pedreschi (Eds), In Knowledge Discovery in Databases: Proceedings 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2004), Pisa, Italy, Springer, pp, 512-514, 2004. |
2003
| Ultsch, A.: Maps for the Visualization of high-dimensional Data Spaces, Proc. Workshop on Self organizing Maps (WSOM), pp. 225-230, Kyushu, Japan, 2003. |
| Ultsch, A.: Pareto Density Estimation: A Density Estimation for Knowledge Discovery, Baier D., Wernecke K.D. (Eds), In Innovations in Classification, Data Science, and Information Systems – Proceedings 27th Annual Conference of the German Classification Society (GfKL) 2003, Berlin, Heidelberg, Springer, pp, 91-100, 2003. |
| Ultsch, A.: Is log ratio a good value for identifying differential expressed genes in microarray experiments?, Technical Report No. 35, Dept. of Mathematics and Computer Science, University of Marburg, Germany, 2003. |
| Ultsch, A.: Optimal Density Estimation in Data containing Clusters of unknown Structure, Technical Report No. 34, Dept. of Mathematics and Computer Science, University of Marburg, Germany, 2003. |
| Ultsch, A.: U*-Matrix: a Tool to visualize Clusters in high dimensional Data, Technical Report No. 36, Dept. of Mathematics and Computer Science, University of Marburg, Germany, 2003. |
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Ultsch, A., Mörchen, F.: Datenbionik, Kooperationspartner in Forschung und InnovationWiesbaden, pp, 25-26, 2003. T. Hain, A. Ultsch, et. al.: MDEAT – a new databionic evaluation and analysis tool to identify the virulence regulon of Listeria monocytogenes as a model system, In Proceedings European Conference on Prokaryotic Genomes, Goettingen, 2003. Mörchen, F.: Time series feature extraction for data mining using DWT and DFT, Technical Report No. 33, Dept. of Mathematics and Computer Science, University of Marburg, Germany, 2003. |
2002
| Ultsch, A.: Emergent Self-Organizing Feature Maps used for Prediction and Prevention of Churn in Mobile Phone Markets, Journal of Targeting, Measurement and Analysis for Marketing 10(4) , pp. 314-324, 2002. |
| Ultsch, A.: Data Mining as an Application for Artificial Life, Polani, D. et al. (Eds), In Abstracting and Synthesizing the Principles of Living Systems, GWAL-5, Lübeck, pp. 191-199, 2002. |
| Ultsch, A.: Proof of Pareto’s 80/20 Law and Precise Limits for ABC-Analysis, Technical Report No. 02/c, Databionics Research Group, University of Marburg, Germany, 2002. |
| Ultsch, A., Eilers, M.: DNA Microarrays von Tumoren diagnostiziert mit datenbionischen Methoden, Kooperationspartner in Forschung und Innovation, Wiesbaden, pp. 19-20, 2002. |
| Ultsch, A., Röske, F.: Self-Organizing Feature Maps Prediciting Sea Levels, Information Sciences 144/Elsevier, pp. 91 – 125, Amsterdam, pp. 1-4, 2002. |
2001
| Ultsch, A.: Fundamentale Aktienanalyse mit selbstorganisierenden Dataminingmethoden, Kooperationspartner in Forschung und Innovation, Wiesbaden, pp. 25-26, 2001. |
| Ultsch, A.: Eine Begründung der Pareto 80/20 Regel und Grenzwerte für die ABC Analyse, Technical Report No. 30, Dept. of Mathematics and Computer Science, University of Marburg, Germany, 2001. |
| Ultsch, A., Rolf, S.: The Completion of Missing Values by Neural Nets for Data Mining, Classification, Automation and New Media, Springer Berlin, pp. 227-234, 2001. |
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Ultsch,A. DataBots: Data Mining as an Application for Autonomous Minirobots, In Proceedings 1st International Conference on Autonomous Minirobots for Research and Edutainment – AMiRE, Paderborn, pp. 59 – 73, 2001. Guimaraes, G., Peter, J., Penzel, T., Ultsch, A.: A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders, In Artificial Intelligence in Medicine 23, Amsterdam, pp. 211-237, 2001. |
2000
| Ultsch, A.: A Neural Network to Compare Highdimensional Data with Skewed and Unknown Distributions, In Proceedings 24. Jahrestagung, Gesellschaft für Klassifikation, Universität Passau, 2000. |
| Ultsch, A.: An Artificial Life Approach to DataMining, In Proceedings European Meeting of Cyberntics and Systems Research EMCSR, Wien, 2000. |
| Ultsch, A.: Clustering with DataBots, In Proceedings Int. Conf. Advances in Intelligent Systems Theory and Applications, AISTA, Canberra, 2000. |
| Ultsch, A.: The Neuronal Data Mine, In Proceedings 2nd Int. ICSC Symposium on Neural Computation NC, Berlin, 2000. |
| Ultsch, A.: Visualization and Classification with Artificial Life, In Proceedings Conf. Int. Fed. of Classification Societies ifcs, Namur, Belgium, pp. 11-14, 2000. |
| Ultsch, A.: Neural Networks Learning Relative Distances, IJCNNInternational Joint Conference on Neural Networks, Como, 2000. |
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Ultsch, A., Rolf, S.: The Completion of Missing Values by Neural Nets for Data Mining, In Proceedings 24. Jahrestagung, Gesellschaft für Klassifikation, Universität Passau, 2000. Deboeck, G. J., Ultsch, A.: Picking Stocks with Emergent Self-Organizing Value Maps, Neural Networks World, Vol 10, Inst. Computer Science, Prague, pp. 203-216, 2000. |
1999
| Ultsch, A.: Data Mining and Knowledge Discovery with Emergent Self-Organizing Feature Maps for Multivariate Time Series, In Oja, E. & Kaski, S. (Eds.), Kohonen maps, (1 ed., pp. 33-46), Elsevier, 1999 |
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Ultsch, A.: Clustering with DataBots, Technical Report No. 19, Dept. of Mathematics and Computer Science, University of Marburg, Germany, 1999. Gabriela Guimaraes, Alfred Ultsch: A method for temporal knowledge conversion, Hand, D.J., Kok, J.N., Berthold, M.R. (Eds), In Advances in Intelligent Data Analysis, Proceedings of the 3rd Int. Symp., Amsterdam, The Netherlands, Sprigner, Berlin, pp. 369-380, 1999. |
1998
| Ultsch, A.: The Integration of Connectionist Models with Knowledge-based Systems: Hybrid Systems, In In Proceedings of the IEEE SMC 98 International Conference, San Diego, pp. 1530-1535, 1998. |
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Ultsch, A.: Hybride Systeme: Der Einsatz von wissensverarbeitenden Systemen, Tagungsband der CoWAN 98 (Cottbusser Workshop Aspekte Neuronalen Lernens), Shaker Verlag, pp. 221-229, 1998. Kleine T. O., Ultsch A.: Neuronal unterstützte Expertensysteme zur Liquoranalytik, Informatik, Biometrie und Epidemiologie in Medizin und Biologie, Vol. 29. München, pp. 12-21, 1998. |
1997
| Ultsch, A., Kleine, T.O., Korus, D., Farsch, S., Guimaraes, G., Pietzuch, W., Simon, J.: Evaluation of Automatic and Manual Knowledge Acquisition for Cerebrospinal Fluid (CSF) Diagnosis, Lecture Notes in Artificial Intelligence in Medicine, AIME’97, Grenoble, Springer, 1997. |
1996
1995
| Ultsch, A, Korus, D., Guimaraes, G., Li, H.: Integration von Neuronalen Netzen mit wissensbasierten Systemen, Reihe Mathematik M-01/, pp. 144-153, 1995. |
| Ultsch, A, Korus, D., Kleine, T. O.: Integration of Neural Networks and Knowledge-Based Systems in Medicine, Barahona, P., Stefanelli, M., Wyatt, J.: Artificial Intelligence in Medicine, Lecture Notes in Artificial Intelligence 934, Springer, pp. 425-426, 1995. |
| Ultsch, A.: Automatic Acquisition of Symbolic Knowledge from Subsymbolic Neural Networks, FORWISS Report, KI-Workshop Informationsfilterung, Erlangen, München, Passau, 1995. |
| Ultsch, A.: Self Organizing Neural Networks perform different from statistical k-means clustering, Gesellschaft für Klassifikation, Basel, 1995. |
| Ultsch, A., Farsch, S., Li, H.: Automatic Acquistion of Medical Knowledge from Data sets with Neural Networks, In Proceedings KI’95, Bielefeld/Germany, Advances in Artificial Ingelligence, Springer, pp. 258-260, 1995. |
| Ultsch, A., Korus, D.: Integration of Neural Networks and Expert Systems in Medicine, Europ. Journal of Clinical Chemistry and Clinical Biochemistry 33(4), pp. A41-A42, 1995. |
| Ultsch, A., Korus, D.: Automatic Acquisition of Symbolic Knowledge from Subsymbolic Neural Networks, In Proceedings 3rd Europ. Congress on Intelligent Techniques and Soft Computing EUFIT’95, Aachen/Germany, AugVol. I, pp. 326-331, pp. 28-31, 1995. |
| Ultsch, A., Korus, D.: Erwerb von Fuzzy-Wissen aus Selbstorganisierenden Neuronalen Netzen, In Proceedings 3. Workshop Fuzzy-Neuro-Systeme’95, Darmstadt/Germany, Novpp. 325-332, pp. 15-17, 1995. |
| Ultsch, A., Korus, D.: Integration of Neural Networks with Knowledge-Based Systems, In Proceedings IEEE-ICNN’95, Perth/Australia, 1995. |
| Ultsch, A., Korus, D., Kleine, T. O.: Neural Networks in Biochemical Analysis, Europ. Journal of Clinical Chemistry and Clinical Biochemistry 33(4), pp. A144-A145, 1995. |
| Ultsch, A., Korus, D., Kleine, T. O.: Integration of Neural Networks and Knowledge Based Systems in Medicine, In Proceedings Compana’95, Würzburg/Germany, Octto be publ. in a special vol. of Chemometrics and Intelligent Laboratory Systems, Elsevier Science, pp. 4-6, 1995. |
| Ultsch, A., Korus, D., Wehrmann, A.: Neural Networks and their Rules for Classification in Marine Geology, In Raum und Zeit in Umweltinformationssystemen, Bd. 7; Proceedings 9th Intl. Symp. Comp. Sci. for Environmental Protection CSEP’95, Berlin, Sept, Vol. I, Metropolis-Verlag, pp. 676-693, 1995. |
| Ultsch,A.: Neuronale Netze zur Interpretation von Daten, alma mater Philippina, Marburger Universitätsbund, pp. 16-19, 1995. |
1994
| Ultsch, A.: Einsatzmöglichkeiten von Neuronalen Netzen im Umweltbereich, Umweltinformatik HdI 13.3, ISDN227238, Oldenbourg Verlag München 1994, pp. 201-226, 1994. |
| Ultsch, A.: The Integration of Neural Networks with Symbolic Knowledge Processing, Diday et al.: New Approaches in Classification and Data Analysis, Springer Verlag, pp. 445-454, 1994. |
| Ultsch, A., Guimaraes, G., Halmans, G.: Self Organizing Neural Networks and hailstorm prediction, Gesellschaft für Klassifikation e.V., Oldenburg, 1994. |
| Ultsch, A., Guimaraes, G., Weber, V.: Self Organizing Feature Maps for Logical Unification, In Proceedings of WCES, Lisboa, 1994. |
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Ultsch, A., Vetter C.: Selforganizing Feature Maps versus Statistical Clustering: A Benchmark, Technical Report No. 9, Dept. of Mathematics and Computer Science, University of Marburg, Germany, 1994. Palm, G., Ultsch, A., Goser, K., Rückert, U.: Knowledge Processing in Neural Architecture, VLSI for Neural Networks and Artificial Intelligence, New York, pp. 207-216, 1994. Schweizer, M., Fohn, P. M. B., Schweizer, J., Ultsch, A.: A hybrid expert system for avalanche forecasting, In Paper presented at the Information and Communications Technologies in Tourism. Proceedings of the International Conference., Innsbruck, Austria, 1994. |
1993
| Ultsch, A.: Self-Organized Feature Maps for Monitoring and Knowledge Acquisition of a Chemical Process, In Proceedings Intl. Conf. on Artificial Neural Networks (ICANN), Amsterdam, SepSpringer-Verlag, pp. 864-867, 1993. | |
| Ultsch, A.: The Integration of Neural Networks with Symbolic Knowledge Processing, In Proceedings Conference of the International Federation of Classification Societies (IFCS-93) in Paris, 1993. | |
| Ultsch, A.: Knowledge Extraction from Self-organizing Neural Networks, Information and Classification, Berlin, Springer, pp. 301-306, 1993. | |
| Ultsch, A.,: Self-organizing Neural Networks for Visualization and Classification, Information and Classification, Berlin, Springer, pp. 307-313, 1993. | |
| Ultsch, A., Guimaraes, G., Korus, D., Li, H.: Knowledge Extraction from Artificial Neural Networks and Applications, Transputer-Anwender-Treffen / World-Transputer-Congress, September 1993, Aachen, Tagungsband TAT/WTC’93, Springer, pp. 194-203, 1993. | |
| Ultsch, A., Li, H.: Automatic Acquisition of Symbolic Knowledge from Subsymbolic Neural Networks, In Proceedings of International Conference on Signal Processing (ICSP’93), Peking, Vol II, pp. 1201-1204, 1993. | |
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Ultsch, A., Mantyk, R., Halmans, G.: A connectionist knowledge acquisition tool CONKAT, Artificial Intelligence Frontiers in Statistics, Chapman & Hall, pp. 256-263, 1993. Palm, G., Ultsch, A., Goser, K., Rückert, U.: Knowledge Processing in Neural Architecture, VLSI for Neural Networks and Artificial Intelligence, Plenum Publ., New York, 1993. |
1992
| Ultsch, A.: Knowledge Acquisition with Self-Organizing Neural Networks, Alexsander, I., Taylor, J. (Eds), In Artificial Neural Networks, Proceedings ICANN, Brighton U.K., pp. 735-740, 1992. |
| Ultsch, A.: Self-Organizing Neural Networks for Knowledge Acquisition, In Proc ECAI, Wien, pp. 208-210, 1992. |
| Ultsch, A.: Self-Organizing Neural Networks for Visualization and Classification, in: Proceedings Conf. Soc. for Information and Classification, Dortmund, 1992. |
| Ultsch, A., Guimaraes, G., Korus, D., Li, H.: Selbstorganisierende Neuronale Netze auf Transputern, In Proceedings TAT 92, Transputer Anwender Treffen, Aachen, Springer, 1992. |
| Ultsch, A., Siemon, H.P.: Kohonen Neural Networks for Exploratory Data Analysis, In Proceedings Conf. Soc. for Information and Classification, Dortmund, 1992. |
1991
| Ultsch, A.: The Integration of Neuronal Networks with Expert Systems, In Proceedings Workshop on Industrial Applications of Neural Networks, Ascona, Vol. III, pp. 3-7, 1991. |
| Ultsch, A.: Konnektionistische Modelle und ihre Integration mit wissensbasierten Systemen, Forschungsbericht Nr. 396, Institut für Informatik, Universität Dortmund, DortmundHabilitationsschrift, 1991. |
| Ultsch, A., Halmans, G.: Data Normalization with Self-Organizing Feature Maps, In Proceedings Intl. Joint Conf. Neural Networks, Seattle, WA, July, Vol. I, pp. 403-407, 1991. |
| Ultsch, A., Halmans, G.: Die Transformation experimenteller Verteilungen durch eine Self-Organizing Feature Map, Ziegler, H. (Eds), In Konnektionismus: Beiträge aus Theorie und Praxis, Proceedings ÖGAI, Wien, pp. 32-40, 1991. |
| Ultsch, A., Halmans, G.: Neuronale Netze zur Unterstützung der Umweltforschung, Symp. Computer Science for Environmental Protection, Munich, Informatik Fachberichte 296, Springer, 1991. |
| Ultsch, A., Halmans, G., Mantyk, R.: A Connectionist Knowledge Acquisition Tool: CONKAT, In Proceedings International Workshop on Artificial Intelligence and Statistics, JanuaryFt. Lauderdale,FL, pp. 2-5, 1991. |
| Ultsch, A., Halmans, G., Mantyk, R.: CONKAT: A Connectionist Knowledge Acquisition Tool, In Proceedings IEEE International Conference on System Sciences, JanuaryHawaiiVol. 1, pp, 507 – 513, pp. 9-11, 1991. |
| Ultsch, A., Halmans, G., Schulz, K.: Die Transformation experimenteller Verteilungen durch eine Self-Organizing Feature Map, Mustererkennung, 13. DAGM Symposium, pp. 207-214, 1991. |
| Ultsch, A., Hannuschka, R., Hartmann, U., Mandischer, M., Weber, V.: Control of Prolog-Proofs using Connectionist Networks, In Proceedings Conf. Information Systems Architecture and Technologies ISAT, Wroclaw, pp. 175-183, 1991. |
| Ultsch, A., Hannuschka, R., Hartmann, U., Mandischer, M., Weber, V.: Optimizing Logical Proofs with Connectionist Networks, In Proceedings Intl. Conf. Artificial Neural Networks, Vol. I, Helsinki, pp. 585-590, 1991. |
| Ultsch, A., Höffgen, K.-U., P.G. ForT2: Automatische Wissensakquisition für Fuzzy-Expertensysteme aus selbstorganisierenden, neuronalen Netzen, Forschungsbericht Nr. 404, Fachbereich Informatik, Universität Dortmund, ISSN, pp. 0933-6192, 1991. |
| Ultsch, A., Höffgen, K.-U., Siemon, H. P.: Genetic Improvements of Feedforward Nets for Approximating Functions, Parallel Problem Solving from Nature, Springer, Berlin, pp. 294-299, 1991. |
| Ultsch, A., Palm, G., Rückert, U.: Wissensverarbeitung in neuronaler Architektur, Verteilte künstliche Intelligenz und kooperatives Arbeiten, GI-Kongress, München, pp. 508-518, 1991. |
| Ultsch, A., Panda, PG.: Die Kopplung konnektionistischer Modelle mit wissensbasierten Systemen, Dortmunder Expertensystemtage 91, TÜV Rheinland, Köln, pp. 74-94, 1991. |
1990
Ultsch, A.: Integrating Control Knowledge in Intelligent Information Retrieval Systems, In Proceedings Int. Workshop Integrierte, intelligente Informationssysteme, Pila, Polen, pp. 325-335, 1990.
Ultsch, A.: Kopplung deklarativer und konnektionistischer Wissensrepräsentation, Endbericht der Projektgruppe PANDA, Forschungsbericht Nr. 352, Institut für Informatik, Universität Dortmund, 1990.
Ultsch, A., Hannuschka, R., Hartmann, U., Mandischer,M., Weber, V.: Connectionist Represented Control Knowledge for Prolog, In Proceedings Neuro Nimes, pp. 559-563, 1990.
Ultsch, A., Hannuschka, R., Hartmann, U., Weber, V.: Learning of Control Knowledge for Symbolic Proofs with Backpropagation Networks, Eckmiller, R., Hartmann, G., Hauske, G. (Eds), In Proceedings Conf. on Parallel Processing in Neural Systems and Computers, Elsevier, North-Holland, Proceedings ICNC, Düsseldorf, pp. 499-502, 1990.
Ultsch, A., Siemon, H.P.: Kohonen’s Self Organizing Feature Maps for Exploratory Data Analysis, In Proceedings Intern. Neural Networks, Kluwer Academic Press, Paris, pp. 305-308, 1990.
1989
1988
| Ultsch, A.: Maschinelles Lernen, Skriptum, KI FS, TH Zürich, 1988. |
1987
1986
Appelrath, H.-J., Ester, M., Jasper, H., Ultsch, A.: KOFIS: An Expert System for Information Retrieval in Offices, The Application of Microcomputers in Information, Documentation and Libraries, Elsevier North Holland, 1986.
Appelrath, H.-J., Ester, M., Ultsch, A.: Knowledge-based Retrieval in an Office, Artificial Intelligence in Economics and Management, North Holland, pp. 269-275, 1986.