Travelled to:1 × Canada
1 × France
1 × Norway
2 × Germany
8 × USA
Collaborated with:H.Kriegel M.Ester X.Xu M.A.Nascimento G.Moise J.Zhou M.M.Breunig A.Coman S.Nassar C.Cheng A.Zimek M.Gaudet R.J.G.B.Campello M.Fokaefs N.Tsantalis A.Chatzigeorgiou S.Rasetic J.Elding R.T.Ng M.Ankerst A.Frommelt M.Wimmer
Talks about:cluster (7) data (5) spatial (3) densiti (3) databas (3) effici (3) detect (3) increment (2) algorithm (2) identifi (2)
Person: Jörg Sander
DBLP: Sander:J=ouml=rg
Contributed to:
2013
20092008
2005
2005
20042003200019991998199819971996Wrote 13 papers:
- KDD-2013-ZimekGCS #detection #effectiveness #performance
- Subsampling for efficient and effective unsupervised outlier detection ensembles (AZ, MG, RJGBC, JS), pp. 428–436.
- ICSM-2009-FokaefsTCS #clustering #object-oriented #using
- Decomposing object-oriented class modules using an agglomerative clustering technique (MF, NT, AC, JS), pp. 93–101.
- KDD-2008-MoiseS #approach #clustering #novel #statistics
- Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering (GM, JS), pp. 533–541.
- CIKM-2005-ComanNS #energy #network #performance #query
- Exploiting redundancy in sensor networks for energy efficient processing of spatiotemporal region queries (AC, MAN, JS), pp. 187–194.
- VLDB-2005-RaseticSEN #performance
- A Trajectory Splitting Model for Efficient Spatio-Temporal Indexing (SR, JS, JE, MAN), pp. 934–945.
- SIGMOD-2004-NassarSC #clustering #effectiveness #incremental #summary
- Incremental and Effective Data Summarization for Dynamic Hierarchical Clustering (SN, JS, CC), pp. 467–478.
- VLDB-2003-ZhouS #clustering #metric
- Data Bubbles for Non-Vector Data: Speeding-up Hierarchical Clustering in Arbitrary Metric Spaces (JZ, JS), pp. 452–463.
- SIGMOD-2000-BreunigKNS #identification #named
- LOF: Identifying Density-Based Local Outliers (MMB, HPK, RTN, JS), pp. 93–104.
- SIGMOD-1999-AnkerstBKS #clustering #identification #named
- OPTICS: Ordering Points To Identify the Clustering Structure (MA, MMB, HPK, JS), pp. 49–60.
- KDD-1998-EsterFKS #algorithm #database #detection
- Algorithms for Characterization and Trend Detection in Spatial Databases (ME, AF, HPK, JS), pp. 44–50.
- VLDB-1998-EsterKSWX #clustering #incremental #mining
- Incremental Clustering for Mining in a Data Warehousing Environment (ME, HPK, JS, MW, XX), pp. 323–333.
- KDD-1997-EsterKSX #database #detection #set
- Density-Connected Sets and their Application for Trend Detection in Spatial Databases (ME, HPK, JS, XX), pp. 10–15.
- KDD-1996-EsterKSX #algorithm #clustering #database #scalability
- A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise (ME, HPK, JS, XX), pp. 226–231.