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Alexander Hinneburg

Senior Researcher and Lecturer

My research concentrates on exploratory data analytics. In particular, I work on clustering and topic modeling as well as applications of those to large databases of documents, molecules  and other complex composed data objects. I combine visualization techniques with machine learning to evaluate and interpret algorithmic results.

Since 2011, I work as senior researcher and lecturer in the database and information systems group at the Martin-Luther-University Halle-Wittenberg. I lead the development of TopicExplorer, a web-based application for visual data exploration of large document collections with topic models.

I give courses and practical exercises on databases, web-technologies at bachelor level and data mining and machine learning as well as information retrieval and visualization at master level.

Research Projects

Publications

1. Anne Purschwitz, Alexander Hinneburg: Funktionsmechanismen  gesellschaftlicher Wissenskonstruktion in der Aufklärung – Chancen und  Grenzen des Topic-Modeling in den Geisteswissenschaften, Die halleschen  Zeitungen und Zeitschriften 1688-1815, in medien & zeit   , akzeptiert zur Publikation, 2019.
2. Christian Papilloud, Alexander Hinneburg: Qualitative Textanalyse mit Topic-Modellen. Eine Einführung für Sozialwissenschaftler, Textbook, Part of the Studienskripten zur Soziologie book series (SSZS), Springer, 2018. DOI: 10.1007/978-3-658-21980-2   .
3. Alexander Hinneburg, Christian Oberländer: Getting  the Story from Big Data: Interaktive visuelle Inhaltsanalyse für die  Sozialwissenschaften mit dem TopicExplorer am Beispiel Fukushima   ,  in Visualisierung sprachlicher Daten, Visual Linguistics – Praxis –  Tools, Noah Bubenhofer, Marc Kupietz (Hrsg.), Heidelberg: Heidelberg  University Publishing, 2018, DOI: 10.17885/heiup.345.474   .
4. Frank Rosner, Alexander Hinneburg: Translating Bayesian Networks into Entity Relationship Models   , in Proceedings ER 2016, Conceptual Modeling – 35th International Conference, 2016, pp. 65-72 (extended version   ).
5. Michael Röder, Andreas Both, Alexander Hinneburg: Exploring the Space of Topic Coherence Measures   , in  Proceeding WSDM '15, Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 2015, pp. 399-408.
6. Alexander Hinneburg, Frank Rosner, Stefan Pessler, Christian Oberländer: Exploring Document Collections with Topic Frames   , Proceeding CIKM '14 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014, pp. 2084-2086.
7. Frank Rosner, Alexander Hinneburg, Michael Röder, Martin Nettling, Andreas Both: Evaluating topic coherence measures   . presented at Topic Models: Computation, Application, and Evaluation, NIPS Workshop 2013.
8. Alexander Hinneburg: Concepts of Visual and Interactive Clustering. Data Clustering   : Algorithms and Applications, in Charu C. Aggarwal, Chandan K. Reddy (Eds.): Data Clustering: Algorithms and Applications. CRC Press 2014: 483-504.
9. André Gohr, Myra Spiliopoulou, Alexander Hinneburg: Visually Summarizing Semantic Evolution in Document Streams with Topic Table   , in Knowledge Discovery, Knowledge Engineering and Knowledge Management (Communications in Computer and Information Science), Springer, 2013:136-150.
10. Alexander Hinneburg, Rico Preiss, René Schröder: TopicExplorer: Exploring Document Collections with Topic Models   . in Peter A. Flach, Tijl De Bie, Nello Cristianini (Eds.): Machine  Learning and Knowledge Discovery in Databases - European Conference,  ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part  II. Springer 2012 Lecture Notes in Computer Science 7524, 2012: 838-841.
11. Frank Rosner, Alexander Hinneburg, Martin Gleditzsch, Matthias Priebe, Andreas Both: Fast sampling word correlations of high dimensional text data (abstract only).SIGMOD Conference 2012: 866
12. Andre Gohr, Alexander Hinneburg, Myra Spiliopoulou, Ricardo Usbeck: On the distinctiveness of tags in collaborative tagging systems   , in Rajendra Akerkar (Ed.): Proceedings  of the International Conference on Web Intelligence, Mining and  Semantics, WIMS 2011, Sogndal, Norway, May 25 - 27, 2011. ACM 2011: 62.
13. Andre Gohr, Myra Spiliopoulou, Alexander Hinneburg: Visually Summarizing the Evolution of Documents under a Social Tag   ,  in Proceedings of International Conference on Knowledge Discovery and  Information Retrieval, KDIR, 2010, SciTePress, ISBN 978-989-8425-28-7,  pp. 85-94.
14. Kristin Mittag, Alexander Hinneburg: Ranked Set Search in Medline Documents,  2. Workshop über Daten in den Lebenswissenschaften.  Informatik-Technologien und Support, GI-Jahrestagung, 2010, Leipzig, LNI  P-176, pp.758-764.
15. Alexander Hinneburg: Visualizing Clustering Results   . Encyclopedia of Database Systems 2009: 3417-3425.
16. Christine Staiger, Alexander Hinneburg, Ralf Bernd Klosgen: Diversity in degrees of freedom of mitochondrial transit peptides   , International Oxford Journal on Molecular Biology and Evolution, 26 (8): 1773-1780, 2009.
17. Andre Gohr, Alexander Hinneburg, Rene Schult, Myra Spiliopoulou: Topic Evolution in a Stream of Documents, 2009 SIAM International Conference on Data Mining (SDM 2009), pp. 859-870.
18. Sebastian Klie, Lennart Martens, Juan Antonio Vizcano, Richard Cote,  Phil Jones,  Rolf Apweiler,  Alexander Hinneburg, Henning Hermjakob: Analyzing Large-Scale Proteomics Projects with Latent Semantic Indexing   , J. Proteome Res., 7 (01), 182191  10.1021/pr070461k, research profile:Turning data graveyards into gold mines   
19. Alexander Hinneburg, Hans-Henning Gabriel, Andre Gohr: Bayesian Folding-In with Dirichlet Kernels for PLSI, IEEE International Conference on Data Mining, ICDM 2007, Omaha, USA, pp. 499-504.
20. Alexander Hinneburg, Andrea Porzel, Karina Wolfram: An Evaluation of Text Retrieval Methods for Similarity Search of multi-dimensional NMR-Spectra, in Proc. of International Conference on Bioinformatics Research and Development, BIRD 2007, LNBI Springer ,Berlin, Germany.
21. Björn Egert, Steffen Neumann, Alexander Hinneburg: Fast approximate Duplicate Detection of 2D-NMR Spectra, in Proc. of International Workshop on Data Integration in the Life Sciences 2007 (DILS'07), LNBI Springer, Philadelphia, USA.
22. Alexander Hinneburg, Hans-Henning Gabriel: DENCLUE 2.0: Fast Clustering based on Kernel Density Estimation, in Proc. of International Symposium on Intelligent Data Analysis 2007 (IDA'07), LNAI Springer, Ljubljana, Slowenien.
23. Sebastian Klie, Lennart Martens, Juan Antonio Vizcaino, Richard  Cote, Phil Jones, Rolf Apweiler, Alexander Hinneburg, Henning Hermjakob:  An application of latent topic document analysis to large-scale proteomics databases, in Proc. of German Bioinformatics Conference 2007 (GCB'07), LNBI Springer, Potsdam, Germany.
24. Alexander Hinneburg, Heikki Mannila, Samuli Kaislaniemi, Terttu Nevalainen, Helena Raumolin-Brunberg: How to Handle Small Samples: Bootstrap and Bayesian Methods in the Analysis of Linguistic Change   , International Oxford Journal on Literary and Linguistic Computing, 2007, doi: 10.1093/llc/fqm006 .
25. Alexander Hinneburg, Björn Egert, Andrea Porzel: Duplicate detection of 2D-NMR Spectra    in Journal of Integrative Bioinformatics, 4(1):53, 2007.
26. Karina Wolfram, Andrea Porzel, Alexander Hinneburg: Similarity Search for multi-dimensional NMR-Spectra of Natural Products,  in Proc. of 10th European Conference on Principles and Practice of  Knowledge Discovery in Databases, PKDD 2006, LNCS Springer ,Berlin,  Germany, pp. 650-658.
27. Alexander Hinneburg, Dirk Habich, Marcel Karnstedt: Analyzing Data Streams by Online DFT,  in Proc. of the International ECML/PKDD Workshop on Knowledge Discovery  from Data Streams, KDDS 2006, Berlin, Germany, pp. 67-76.
28. Stefan Brass, Alexander Hinneburg: Tagungsband: Grundlagen von Datenbanken, GvD 2006, Wittenberg, Juni 2006.
29. Alexander Hinneburg, Andreas Hotho, Ralf Klinkenberg: Tagungsband: Knowledge Discovery, Data Mining und Maschinelles Lernen, KDML 2006   ,  Hildesheim, 2006.
30. A. Gionis, A. Hinneburg, S. Papadimitriou, P. Tsaparas: Dimension induced clustering, KDD 2005: 51-60, ACM. (Talk)
31. D. Habich, W. Lehner, A. Hinneburg: Optimizing Multiple Top-K Queries over Joins. SSDBM 2005: 195-204, IEEE.
32. D. Habich, W. Lehner, A. Hinneburg, P. Kitzmantel, M. Kimpl: Eyes4Ears - More than a Classical Music Retrieval System. MUSICNETWORK 2005   
33. M. Heczko, A. Hinneburg, D. Keim, M. Wawryniuk, Multiresolution similarity search in image databases   , Multimedia Syst. 10(1): 28-40 (2004)
34. Alexander Hinneburg, Matthias Fischer, Falk Bahner: Finding frequent substructures in 3D-protein databases, in Proc. of the Workshop on Bioinformatics in conjunction with ICDE 2003, Bangalore India.
35. Alexander Hinneburg, Daniel A. Keim, Markus Wawryniuk: Using Projections to visually cluster high-dimensional Data   , in IEEE Journal of Computing in Science and Engineering, Vol.5, No.2, March/April 2003, pp. 12-25.
36. Alexander Hinneburg, Wolfgang Lehner: Database Support for 3D-Protein Data Set Analysis   ,  in Proceedings of the 15th International Conference on Scientific and  Statistical Database Management, 2003, Cambridge, Massachusetts, USA.  IEEE Computer Society 2003, pp. 161-170.
37. Alexander Hinneburg, Wolfgang Lehner, Dirk Habich: COMBI-Operator: Database Support for Data Mining   , in Proceedings of the 29th International Conference on Very Large Databases, VLDB 2003, Berlin, Germany.
38. Alexander Hinneburg and Daniel A. Keim: A General Approach to Clustering in Large Databases with Noise    in Journal of Knowledge and Information Systems (KAIS), Vol. 5, No. 4, pp. 387-415, Springer 2003.
39. Hinneburg A., Keim D.: Using Visual Interaction to solve Complex Optimization Problems, in Data Visualization: The State of the Art 2003: 407-422.
40. Daniel A. Keim, Martin Heczko, Alexander Hinneburg, Markus Wawryniuk: Multi-Resolution Similarity Search in Image Databases   , in Proceedings of International Workshop on Multimedia Information Systems, 2002, Tempe, Arizona, USA, pp.76-85.
41. Aggarwal C., Hinneburg A., Keim D.A.: On the Surprising Behavior of Distance Metrics in High Dimensional Space,in Proc. of 8th International Conference on Database Theory, ICDT 2001, London, pp. 420-434.
42. Hinneburg A., Keim D.A., and Brandt W.:Clustering 3D-structures of Small Aminoacid-chains for Detecting Dependence from Their Sequential Context in Proteins   ,  in  Proc. of IEEE International Symposium on Bio-Informatics &  Biomedical Engineering (BIBE 2000),Washington DC, 2000, pp.43-49. PDF-Version (1.4MB)
43. Hinneburg A., Keim D.A.:Werkzeuge zur interaktiven Clusteranalyse, in  Proc. of the annual meeting of the GI Machine Learning Group (FGML 2000), GMD Report 114, Bonn, 2000. (Abstract in German) PDF-Version (100kB)
44. Hinneburg A., Aggarwal C., Keim D.A.:What is the nearest neighbor in highdimensional spaces, in  Proc. 26th Int. Conf. on Very Large Data Bases, Cairo, 2000. PDF-Version (300kB)
45. Hinneburg A., Keim D. A.: Clustering Techniques for Large Data Sets: From the Past to the Future ,Tutorial, Proc. Int. Conf. on Principles and Practize in Knowledge Discovery (PKDD'00), Lyon, France, 2000.
46. Hinneburg A.: High Dimensional Clustering on Large Data Sets, in  Proc. of the EDBT 2000 PhD Workshop   , 2000. PDF-Version (57kB)
47. Hinneburg A., Keim  D.A., Wawryniuk M.:HD-Eye: Visual Mining High-dimensional Data , in IEEE Computer Graphics and Applications, Sept/Oct. 1999, Vol. 19 (5), pp.22-31.
48. Hinneburg A., Keim D. A.: Cluster Discovery Methods for Large Data Bases: From the Past to the Future , Tutorial, Proc. ACM SIGMOD Int. Conf. on Management of Data, Philadelphia, PA, 1999.
49. Hinneburg A., Keim D. A.: Clustering Techniques for Large Data Sets: From the Past to the Future ,Tutorial, Proc. Int. Conf. on Knowledge Discovery in Databases (KDD'99), San Diego, CA, 1999.
50. Hinneburg A., Keim  D.A.:Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering , in  Proc. 25th Int. Conf. on Very Large Data Bases, Edinburgh, 1999, pp.506-517. Postscript-Version (570kB)
51. Hinneburg A., Keim  D.A.: An Efficient Approach to Clustering in Multimedia Databases with Noise, Proc. 4rd Int. Conf. on Knowledge Discovery and Data Mining, New York, AAAI Press, 1998.   Postscript-Version (800k)

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