Research

Short CV: In 2002 Fabian Moerchen graduated in from the University of Wisconsin, Milwaukee with the Master of Science in mathematics and computer science. He joined the Databionics Research Group at the Philipps-University of Marburg, Germany, from which he graduated with the PhD in 2006. He is currently working for Siemens Corporate Research in Princeton, NJ.

Projects:

Time Series Knowledge Mining	Mining of interpretable patterns in multivariate interval time series.
Temporal Text Mining	Using temporal data mining to explore document streams and archives.
Music Mining	Description of sound and visualization of music collections.
Persist time series discretization	Detecting meaningful states in numerical time series.
DWT/DFT time series feature selection	Optimal features selection for mining a set of time series.
Neural Nets on Beowulf	Speed up of artificial neural nets on Beowulf clusters.

Publications:

2010

Mörchen, F., Fradkin, D.: Robust mining of time intervals with semi-interval partial order patterns, In Proceedings SIAM Conference on Data Mining, (2010)

2009

Dejori, M., Malik, H.H., Mörchen, F., Neubauer, C.: Development of data infrastructure for the Long Term Bridge Performance program, In Proceedings of the Structures Congress (ASCE), (2009)

2008

Mörchen, F.: Organic pie charts, In Proceedings IEEE International Conference on Data Mining, 30, (2008), pp. 947-952

Mörchen, F., Fradkin, D., Dejori, M., Wachmann, B.: Emerging trend prediction in biomedical literature, In Proceedings American Medical Informatics Association (AMIA) 2008 Annual Symposium, 29, (2008) PubMed

Yu, Y., Downie, J.S., Mörchen, F., Chen, L., Joe, K.: Using Exact Locality Sensitive Mapping to Group and Detect Audio-Based Cover Songs, In Proceedings 10th IEEE International Symposium on Multimedia (ISM), IEEE Computer Society, (2008), pp. 302-309 IEEE

Yu, Y., Downie, J.S., Mörchen, F., Chen, L., Joe, K., Oria, V.: COSIN: content-based retrieval system for cover songs, Abdulmotaleb El-Saddik and Son Vuong and Carsten Griwodz and Alberto Del Bimbo and K. Selcuk Candan and Alejandro Jaimes (Eds), In Proceedings 16th ACM International Conference on Multimedia, ACM, (2008), pp. 987-988 ACM

Mörchen, F., Dejori, M., Fradkin, D., Etienne, J., Wachmann, B., Bundschus, M.: Anticipating annotations and emerging trends in biomedical literature, In Proceedings Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 26, (2008), pp. 954-962

Renner, S., Derksen, S., Radestock, S., Mörchen, F.: Maximum Common Binding Modes (MCBM): Consensus Docking Scoring Using Multiple Ligand Information and Interaction Fingerprints, Journal of Chemical Information and Modeling ACS Publications, (2008) ACS

2007

Weihs, C., Ligges, U., Mörchen, F., Müllensiefen, D.: Classification in Music Research, Advances in Data Analysis and Classification 1(3)Springer, (2007), pp. 255-291 SpringerLink

Mörchen, F., Brinker, K., Neubauer, C.: Any-time clustering of high frequency news streams, In Proceedings Data Mining Case Studies Workshop (DMCS), The Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,, San Jose, CA, USA, 23, (2007)

Risi, S., Mörchen, F., Ultsch, A., Lewark, P.: Visual mining in music collections with Emergent SOM, In Proceedings Workshop on Self-Organizing Maps (WSOM), Bielefeld, Germany, (2007)

Mörchen, F.: Unsupervised pattern mining from symbolic temporal data, SIGKDD Explorations 9(1)ACM, (2007), pp. 41-55 ACM

Mörchen, F., Ultsch, A.: Efficient Mining Of Understandable Patterns From Multivariate Interval Time Series, Data Mining and Knowledge Discovery 15(2)Springer, (2007), pp. 181-215 SpringerLink

2006

Mörchen, F., Mierswa, I., Ultsch, A.: Understandable Models Of Music Collections Based On Exhaustive Feature Generation With Temporal Statistics, Tina Eliassi-Rad, Lyle H. Ungar, Mark Craven, Dimitrios Gunopulos (Eds), In Proceedings The Twelveth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, (2006), pp. 882-891 ACM Digital Library

Mörchen, F.: A better tool than Allen's relations for expressing temporal knowledge in interval data, In Proceedings Temporal Data Mining Workshop (TDM), The Twelveth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, (2006)

Mörchen, F.: Algorithms For Time Series Knowledge Mining, Tina Eliassi-Rad, Lyle H. Ungar, Mark Craven, Dimitrios Gunopulos (Eds), In Proceedings The Twelveth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, (2006), pp. 668-673 ACM Digital Library

Mörchen, F.: Time Series Knowledge Mining, Phd Thesis, Philipps-University Marburg, Germany, Görich & Weiershäuser, Marburg, Germany, (2006), pp. 180 ISBN 3-89703-670-3

Nöcker, M., Mörchen, F., Ultsch, A.: Fast and reliable ESOM learning, M. Verleysen (Eds), In Proceedings 14th European Symposium on Artificial Neural Networks (ESANN'06), Bruges, Belgium, (2006), pp. 131-136

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 Press, (2006), pp. 81-90 IEEE Xplore

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)(2006), pp. 197-208

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, (2005), pp. 396-403

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, (2005), pp. 660-665 ACM Digital Library

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)

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), Springer, Heidelberg, (2005), pp. 724-731

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, (2005), pp. 278-285 url SpringerLink

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)

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, (2005), pp. 272-279

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, (2004), pp. 512-514

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, (2004), pp. 127-140

Mörchen, F.: Analysis of speedup as function of block size and cluster size for parallel feed-forward neural networks on a Beowulf cluster, IEEE Transaction on Neural Networks 15(2)(2004), pp. 515-527 IEEE Xplore

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)

Research
Time Series Knowledge Mining
Temporal Text Mining
Music Mining
Persist Time Series Discretization
DWT/DFT Feature Selection
Neural Nets on Beowulf
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Copyright © 2006 Fabian Moerchen.