Publikationen

11 Ergebnisse

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]
Hassani M., Kim Y., Seungjin Choi, Seidl T.:
Effective Evaluation Measures for Subspace Clustering of Data Streams
The third Quality issues, measures of interestingness and evaluation of data mining models workshop (QIMIE'13), held in conjunction with PAKDD'13 conference (17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Gold Coast, Australia) S.342-353 (2013)
[QIMIE13] [PAKDD13]
Hassani M., Kim Y., Seidl T.:
Subspace MOA: Subspace Stream Clustering Evaluation Using the MOA Framework
The 18th International Conference on Database Systems for Advanced Applications (DASFAA 2013), Wuhan, China (Best Demo Award Runner-Up) S.446-449 (2013) (Demo)
[DASFAA 2013]

2012

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 S.309-313 (2012) (Demo)
[DASFAA 2012]

2011

Kranen P.:
Anytime Algorithms for Stream Data Mining
Dissertation, Fakultät für Mathematik, Informatik und Naturwissenschaften, RWTH Aachen University (2011) Tag der mündlichen Prüfung: 14.09.2011
[[RWTH Bibliothek]] [urn:nbn:de:hbz:82-opus-38501]
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 S.868-876 (2011)
[KDD 2011] [DOI: 10.1145/2020408.2020555]
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 S.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 S.44-50 (2010)
[www.pascal-network.org/wapa2010] [JMLR Proc. Vol. 11] [pdf]
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). S.3-16 (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 S.1400-1403 (2010) (Demo)
[ICDM 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]