Publications

19 results

2013

Kremer H.:
Mining and Similarity Search in Temporal Databases
Dissertation, Fakultät für Mathematik, Informatik und Naturwissenschaften, RWTH Aachen University. (2013) Tag der mündlichen Prüfung: 14.10.2013
[PDF] [RWTH Bibliothek] [Apprimus-Verlag]
Kremer H., Günnemann S., Held A., Seidl T.:
An Evaluation Framework for Temporal Subspace Clustering Approaches
Proc. IEEE International Conference on Data Mining Workshops (ICDMW), Dallas, TX, USA  P.1089-1092 (2013) (Demo)
[ICDM 2013]
Kremer H., Günnemann S., Wollwage S., Seidl T.:
Nesting the Earth Mover's Distance for Effective Cluster Tracing
Proc. of the 25th International Conference on Scientific and Statistical Database Management (SSDBM), Baltimore, Maryland, USA P.34:1-34:4 (2013)
[SSDBM 2013]

2012

Günnemann S., Kremer H., Laufkötter C., Seidl T.:
Tracing Evolving Subspace Clusters in Temporal Climate Data
Data Mining and Knowledge Discovery Journal (DMKD), Vol. 24, Nr. 2 P.387-410 (2012)
[Full Text PDF (Springer Open Access)]
Kremer H., Günnemann S., Held A., Seidl T.:
Effective and Robust Mining of Temporal Subspace Clusters
Proc. IEEE International Conference on Data Mining (ICDM), Brussels, Belgium P.369-378 (2012)
[ICDM 2012]
Kremer H., Günnemann S., Held A., Seidl T.:
Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases
Proceedings 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Kuala Lumpur, Malaysia P.444-455 (2012)  
[PAKDD 2012] [full text PDF]
Günnemann S., Kremer H., Musiol R., Haag R., Seidl T.:
A Subspace Clustering Extension for the KNIME Data Mining Framework
Proc. IEEE International Conference on Data Mining Workshops (ICDMW), Brussels, Belgium P.886-889 (2012) (Demo)
[ICDM 2012] [Download Page]
Kranen P., Kremer H., Jansen T., Seidl T., Bifet A., Holmes G., Pfahringer B., Read J.:
Stream Data Mining using the MOA Framework
The 17th International Conference on Database Systems for Advanced Applications (DASFAA), Busan, South Korea P.309-313 (2012) (Demo)
[DASFAA 2012]

2011

Kremer H., Kranen P., Jansen T., Seidl T., Bifet A., Holmes G., Pfahringer B.:
An Effective Evaluation Measure for Clustering on Evolving Data Streams
Proc. of the 17th ACM Conference on Knowledge Discovery and Data Mining (SIGKDD 2011), San Diego, CA, USA P.868-876 (2011)
[KDD 2011] [DOI: 10.1145/2020408.2020555]
Günnemann S., Kremer H., Laufkötter C., Seidl T.:
Tracing Evolving Clusters by Subspace and Value Similarity
Proc. 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2011), Shenzhen, China P.444-456 (2011)
[PAKDD 2011]
Bifet A., Holmes G., Pfahringer B., Read J., Kranen P., Kremer H., Jansen T., Seidl T.:
MOA: a Real-time Analytics Open Source Framework
Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2011), Athens, Greece P.617-620 (2011) (Demo)
[ECML PKDD 2011]

2010

Bifet A., Holmes G., Pfahringer B., Kranen P., Kremer H., Jansen T., Seidl T.:
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering
Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings, Volume 11: Workshop on Applications of Pattern Analysis P.44-50 (2010)
[www.pascal-network.org/wapa2010] [JMLR Proc. Vol. 11] [pdf]
Günnemann S., Kremer H., Seidl T.:
Subspace Clustering for Uncertain Data
Proc.  SIAM International Conference on Data Mining (SDM 2010), Columbus, Ohio, USA. P.385-396 (2010)
[SDM 2010]
Kremer H., Günnemann S., Seidl T.:
Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques
Proc. 2nd IEEE ICDM Workshop on Knowledge Discovery from Climate Data: Prediction, Extremes, and Impacts (CLIMKD 2010) in conjunction with IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia P.96-97 (2010)
[ICDM 2010] [CLIMKD 2010]
Bifet A., Holmes G., Pfahringer B., Kranen P., Kremer H., Jansen T., Seidl T.:
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering
Invited presentation at the International Workshop on Handling Concept Drift in Adaptive Information Systems in conjunction with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010). P.3-16 (2010)
Günnemann S., Kremer H., Färber I., Seidl T.:
MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions
Proc. IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia P.1387-1390 (2010) (Demo)
[ICDM 2010]
Kranen P., Kremer H., Jansen T., Seidl T., Bifet A., Holmes G., Pfahringer B.:
Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA
Proc. IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia P.1400-1403 (2010) (Demo)
[ICDM 2010]
Günnemann S., Färber I., Kremer H., Seidl T.:
CoDA: Interactive Cluster Based Concept Discovery
Proceedings of the VLDB Endowment (PVLDB) 3(2): 1633-1636 (2010) P.1633-1636 (2010) (Demo)
[VLDB 2010]
Kranen P., Kremer H., Jansen T., Seidl T., Bifet A., Holmes G., Pfahringer B.:
Benchmarking Stream Clustering Algorithms within the MOA Framework
16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010), Washington, DC, USA (2010) 2010 KDD - MOA (demo).pdf(Demo)
[KDD 2010]