Publikationen

16 Ergebnisse

2014

Assam R., Seidl T.:
Check-in Location Prediction using Wavelets and Conditional Random Fields
Proc. IEEE International Conference on Data Mining (ICDM 2014), Shenzhen, China (2014)
[ICDM 2014]

2013

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] [supplementary material]
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]

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]

2011

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]

2010

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]
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]
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]

2009

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]

2008

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]

2007

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]