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Данные linkpad ( 12 Сентября 2014 ) | |
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Внешние ссылки главной страницы ( 43 ) | |
uwm.edu | University of Wisconsin, Milwaukee |
uni-marburg.de/fb12/datenbionik/staff | Prof. Dr. Ultsch |
uni-marburg.de/index_html-en?set_language=en | Philipps-University of Marburg |
usa.siemens.com/en/about_us/research/home.htm | Siemens Corporate Research |
amazon.com | Amazon |
linkedin.com/in/fabianmoerchen | |
scholar.google.com/citations?user=noJzf90AAAAJ | Google Scholar |
informatik.uni-trier.de/~ley/pers/hd/m/Moerchen:Fabian.html | DBLP |
timeseriesknowledgemining.org | Time Series Knowledge Mining |
astro.temple.edu/ tua63862/indg0371-sipos.pdf | |
win.tue.nl/ lamthuy/MyPub/sadm.pdf | |
tods.acm.org/accepted/2013/TattiFinding.pdf | TODS |
home.eng.iastate.edu/ bibudh/techreport/burst_detection.pdf | Technical Report |
poloclub.gatech.edu/idea2013/papers/p55-lam.pdf | IDEA |
people.cs.pitt.edu/ iyad/papers/KDD_2012.pdf | |
icml.cc/2012/papers/188.pdf | ICML |
ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumbe | IEEE |
siam.org/meetings/sdm11/tutorials.php | SDM 2011 |
dx.doi.org/10.1007/s10115-010-0329-5 | Springer |
dx.doi.org/10.1007/s10115-010-0317-9 | Springer |
kdd.org/kdd2010/tutorials.shtml#t3 | KDD 2012 |
springerlink.com/content/m52160v03u8t3pt5/? | Springer |
ncbi.nlm.nih.gov/pmc/articles/PMC2656084/pdf/amia-0485-s2008... | |
dx.doi.org/10.1109/ISM.2008.18 | IEEE |
doi.acm.org/10.1145/1459359.1459542 | ACM |
dx.doi.org/10.1021/ci7003626 | ACS |
dx.doi.org/10.1007/s11634-007-0016-x | SpringerLink |
acm.org/sigs/sigkdd/explorations/issues/9-1-2007-06/moerchen... | ACM |
dx.doi.org/10.1007/s10618-007-0070-1 | SpringerLink |
doi.acm.org/10.1145/1150402.1150523 | ACM Digital Library |
doi.acm.org/10.1145/1150402.1150485 | ACM Digital Library |
dx.doi.org/10.1109/TSA.2005.860352 | IEEE Xplore |
doi.acm.org/10.1145/1081870.1081953 | ACM Digital Library |
dx.doi.org/10.1007/3-540-31314-1_33 | SpringerLink |
dx.doi.org/10.1109/TNN.2004.824264 | IEEE Xplore |
mathworks.com | Matlab |
download.thiesm.de/src/science/jtskm_0.1.zip | Java implementation |
thiesm.de | Michael Thies |
databionic-esom.sf.net | ESOM |
java.com | Java |
musicminer.sf.net | MusicMiner |
mpich.org | MPICH |
gnu.org/software/gsl/ | GSL |
Внутренние ссылки главной страницы ( 39 ) | |
papers/lam12mining.pdf | Mining Compressing Sequential Patterns |
papers/tatti11finding.pdf | Finding Robust Itemsets Under Subsampling |
papers/moerchen11tutorial.pdf | Temporal pattern mining in symbolic time point and time interval data |
papers/moerchen10efficient.pdf | Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compressi... |
papers/moerchen10tutorial.pdf | Temporal pattern mining in symbolic time point and time interval data |
papers/fradkin10margin.pdf | Margin-Closed Frequent Sequential Pattern Mining |
papers/moerchen10robust.pdf | Robust mining of time intervals with semi-interval partial order patterns |
comp.nus.edu.sg/ yuy/IJSC_0302_P209.pdf | |
papers/moerchen08organic.pdf | Organic pie charts |
papers/moerchen08anticipating.pdf | Anticipating annotations and emerging trends in biomedical literature |
papers/weihs07classification.pdf | Classification in Music Research |
papers/moerchen07anytime.pdf | Any-time clustering of high frequency news streams |
papers/risi07visual.pdf | Visual mining in music collections with Emergent SOM |
papers/moerchen07unsupervised.pdf | Unsupervised pattern mining from symbolic temporal data |
papers/moerchen07efficient.pdf | Efficient Mining Of Understandable Patterns From Multivariate Interval Time Series |
papers/moerchen06understandable.pdf | Understandable Models Of Music Collections Based On Exhaustive Feature Generation With Temporal Statistics |
papers/moerchen06tdm.pdf | A better tool than Allen's relations for expressing temporal knowledge in interval data |
papers/moerchen06kdd.pdf | Algorithms For Time Series Knowledge Mining |
papers/moerchen06tskm.pdf | Time Series Knowledge Mining |
#contact | Contact |
papers/noecker06fast.pdf | Fast and reliable ESOM learning |
papers/moerchen06modelling.pdf | Modelling timbre distance with temporal statistics from polyphonic music |
papers/moerchen06extracting.pdf | Extracting interpretable muscle activation patterns with Time Series Knowledge Mining |
papers/moerchen05ismir.pdf | Databionic visualization of music collections according to perceptual distance |
papers/moerchen05optimizing.pdf | Optimizing Time Series Discretization for Knowledge Discovery |
papers/ultsch05esom.pdf | ESOM-Maps: tools for clustering, visualization, and classification with Emergent SOM |
papers/moerchen05visual.pdf | Visual mining in music collections |
papers/moerchen05finding.pdf | Finding persisting states for knowledge discovery in time series |
papers/moerchen05musicminer.pdf | MusicMiner: Visualizing timbre distances of music as topographical maps |
papers/moerchen04gfkl.pdf | Discovering Temporal Knowledge in Multivariate Time Series |
papers/moerchen04muscle.pdf | Discovering interpretable muscle activation patterns with the Temporal Data Mining Method |
papers/moerchen04mining.pdf | Mining Hierarchical Temporal Patterns in Multivariate Time Series |
papers/moerchen04analysis.pdf | Analysis of speedup as function of block size and cluster size for parallel feed-forward neural networks on a Beowulf cluster |
papers/moerchen03time.pdf | Time series feature extraction for data mining using DWT and DFT |
sipo.zip | Intervals |
tskm.zip | TSKM |
persist.zip | Persist |
dwt.zip | DWT |
beowulf.tar.gz | Beowulf |
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