Publikationen

18 Ergebnisse

Tutorials

Müller E., Günnemann S., Färber I., Seidl T.:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
Tutorial at IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia S.1220 (2010)
[ICDM 2010] [Tutorial Website]

Conference papers (peer reviewed)

Günnemann S., Färber I., Raubach S., Seidl T.:
Spectral Subspace Clustering for Graphs with Feature Vectors
Proc. IEEE International Conference on Data Mining (ICDM), Dallas, TX, USA S.231-240 (2013)
[ICDM 2013]
Zimmer (née Ivanescu) A., Kurze M., Seidl T.:
Adaptive Model Tree for Streaming Data
Proc. IEEE International Conference on Data Mining (ICDM), Dallas, TX, USA S.1319-1324 (2013) AdaptiveModelTreePaper.pdf
[ICDM 2013]
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  S.1089-1092 (2013) (Demo)
[ICDM 2013]
Zimmer (née Ivanescu) A., Albin T., Abel D., Seidl T.:
Employing the Principal Hessian Direction for Building Hinging Hyperplane Models
Workshop on Optimization Based Techniques for Emerging Data Mining (OEDM 2012) in conjunction with the IEEE International Conference on Data Mining (ICDM 2012), Brussels, Belgium. S.481-485 (2012) HHmodelsPrincipalHessianDirection.pdf
[ICDM 2012]
Günnemann S., Dao P., Jamali M., Ester M.:
Assessing the Significance of Data Mining Results on Graphs with Feature Vectors
Proc. IEEE International Conference on Data Mining (ICDM 2012), Brussels, Belgium S.270-279 (2012)
[ICDM 2012]
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 S.369-378 (2012)
[ICDM 2012]
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 S.886-889 (2012) (Demo)
[ICDM 2012] [Download Page]
Günnemann S., Müller E., Raubach S., Seidl T.:
Flexible Fault Tolerant Subspace Clustering for Data with Missing Values
Proc. IEEE International Conference on Data Mining (ICDM 2011), Vancouver, Canada (2011)
[ICDM 2011]
Günnemann S., Färber I., Boden B., Seidl T.:
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms
Proc. IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia S.845-850 (2010)
[ICDM 2010] [Supplementary material]
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 S.96-97 (2010)
[ICDM 2010] [CLIMKD 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 S.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 S.1400-1403 (2010) (Demo)
[ICDM 2010]
Kranen P., Assent I., Baldauf C., Seidl T.:
Self-Adaptive Anytime Stream Clustering
Proc. IEEE International Conference on Data Mining (ICDM 2009), Miami, USA S.249-258 (2009) (full paper acceptance rate 8.9%)
[ICDM 2009] [DOI: 10.1109/ICDM.2009.47]
Müller E., Assent I., Günnemann S., Krieger R., Seidl T.:
Relevant Subspace Clustering: Mining the Most Interesting Non-Redundant Concepts in High Dimensional Data
Proc. IEEE International Conference on Data Mining (ICDM 2009), Miami, USA S.377-386 (2009) (full paper acceptance rate 8.9%)
[ICDM 2009] [Supplementary material]
Assent I., Krieger R., Müller E., Seidl T.:
INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy
Proc. IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy S.719-724 (2008) (acceptance rate 20%)
[ICDM 2008]
Assent I., Krieger R., Müller E., Seidl T.:
DUSC: Dimensionality Unbiased Subspace Clustering
Proc. IEEE International Conference on Data Mining (ICDM 2007), Omaha, Nebraska, USA S.409-414 (2007) (acceptance rate 19%)
[ICDM 2007]
Assent I., Krieger R., Glavic B., Seidl T.:
Spatial Multidimensional Sequence Clustering
Proc. 1st International Workshop on Spatial and Spatio-temporal Data Mining (SSTDM 2006)In conjunction with ICDM 2006, Hong Kong S.343-348 (2006)
[SSTDM 2006]