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Philipp Kranen
Philipp Kranen
Microsoft Research
Verified email at microsoft.com
Title
Cited by
Cited by
Year
Moa: Massive online analysis, a framework for stream classification and clustering
A Bifet, G Holmes, B Pfahringer, P Kranen, H Kremer, T Jansen, T Seidl
Proceedings of the first workshop on applications of pattern analysis, 44-50, 2010
23012010
An introduction to computational networks and the computational network toolkit
D Yu, A Eversole, M Seltzer, K Yao, Z Huang, B Guenter, O Kuchaiev, ...
Microsoft Technical Report MSR-TR-2014–112, 2014
4762014
The clustree: indexing micro-clusters for anytime stream mining
P Kranen, I Assent, C Baldauf, T Seidl
Knowledge and information systems 29, 249-272, 2011
3512011
An effective evaluation measure for clustering on evolving data streams
H Kremer, P Kranen, T Jansen, T Seidl, A Bifet, G Holmes, B Pfahringer
Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011
1392011
Anyout: Anytime outlier detection on streaming data
I Assent, P Kranen, C Baldauf, T Seidl
Database Systems for Advanced Applications: 17th International Conference …, 2012
1342012
Self-adaptive anytime stream clustering
P Kranen, I Assent, C Baldauf, T Seidl
2009 Ninth IEEE International Conference on Data Mining, 249-258, 2009
912009
Efficient emd-based similarity search in multimedia databases via flexible dimensionality reduction
M Wichterich, I Assent, P Kranen, T Seidl
Proceedings of the 2008 ACM SIGMOD international conference on Management of …, 2008
772008
MOA: a real-time analytics open source framework
A Bifet, G Holmes, B Pfahringer, J Read, P Kranen, H Kremer, T Jansen, ...
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011
702011
Indexing density models for incremental learning and anytime classification on data streams
T Seidl, I Assent, P Kranen, R Krieger, J Herrmann
Proceedings of the 12th international conference on extending database …, 2009
692009
Harnessing the strengths of anytime algorithms for constant data streams
P Kranen, T Seidl
Data Mining and Knowledge Discovery 19, 245-260, 2009
412009
Clustering performance on evolving data streams: Assessing algorithms and evaluation measures within moa
P Kranen, H Kremer, T Jansen, T Seidl, A Bifet, G Holmes, B Pfahringer
2010 IEEE International Conference on Data Mining Workshops, 1400-1403, 2010
332010
Stream data mining using the MOA framework
P Kranen, H Kremer, T Jansen, T Seidl, A Bifet, G Holmes, B Pfahringer, ...
Database Systems for Advanced Applications: 17th International Conference …, 2012
322012
Pipeline generation for data stream actuated control
ADB De Septfontaines, M Cozowicz, P Kranen, T Santen
US Patent App. 14/573,866, 2016
302016
Massive online analysis manual
A Bifet, R Kirkby, P Kranen, P Reutemann
University of Waikato, New Zealand: Centre for Open Software Innovation 11 …, 2009
302009
Precise anytime clustering of noisy sensor data with logarithmic complexity
M Hassani, P Kranen, T Seidl
Proceedings of the Fifth International Workshop on Knowledge Discovery from …, 2011
282011
Subspace anytime stream clustering
M Hassani, P Kranen, R Saini, T Seidl
Proceedings of the 26th International Conference on Scientific and …, 2014
202014
MC-tree: Improving bayesian anytime classification
P Kranen, S Günnemann, S Fries, T Seidl
Scientific and Statistical Database Management: 22nd International …, 2010
202010
Mobile mining and information management in healthnet scenarios
P Kranen, D Kensche, S Kim, N Zimmermann, E Müller, C Quix, X Li, ...
The Ninth International Conference on Mobile Data Management (mdm 2008), 215-216, 2008
172008
Detection of anomalies in error signals of cloud based service
O Ivanova, S Ojha, A De Baynast, M Cozowicz, U Pinsdorf, Y Wang, ...
US Patent 9,378,079, 2016
142016
BT*–An Advanced Algorithm for Anytime Classification
P Kranen, M Hassani, T Seidl
International Conference on Scientific and Statistical Database Management …, 2012
112012
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