Travelled to:1 × China
1 × Egypt
1 × Germany
1 × Spain
1 × United Kingdom
5 × USA
Collaborated with:D.A.Keim F.Rosner A.Gohr M.Spiliopoulou W.Lehner D.Habich M.Wawryniuk C.C.Aggarwal S.Peßler C.Oberländer A.Gionis S.Papadimitriou P.Tsaparas M.Gleditzsch M.Priebe A.Both
Talks about:cluster (7) dimension (5) high (4) data (4) databas (3) larg (3) document (2) visual (2) futur (2) past (2)
Person: Alexander Hinneburg
DBLP: Hinneburg:Alexander
Contributed to:
2014
2012
2010
2005
2003200220001999199919991998Wrote 11 papers:
- CIKM-2014-HinneburgRPO #documentation #topic
- Exploring Document Collections with Topic Frames (AH, FR, SP, CO), pp. 2084–2086.
- SIGMOD-2012-RosnerHGPB #correlation #performance #word
- Fast sampling word correlations of high dimensional text data (FR, AH, MG, MP, AB), p. 866.
- KDIR-2010-GohrSH #documentation #evolution #social #visual notation
- Visually Summarizing the Evolution of Documents under a Social Tag (AG, MS, AH), pp. 85–94.
- KDD-2005-GionisHPT #clustering
- Dimension induced clustering (AG, AH, SP, PT), pp. 51–60.
- VLDB-2003-HinneburgLH #data mining #database #mining #named
- COMBI-Operator: Database Support for Data Mining Applications (AH, WL, DH), pp. 429–439.
- SIGMOD-2002-HinneburgKW #clustering #named #visual notation
- HD-Eye: visual clustering of high dimensional data (AH, DAK, MW), p. 629.
- VLDB-2000-HinneburgAK #nearest neighbour #question #what
- What Is the Nearest Neighbor in High Dimensional Spaces? (AH, CCA, DAK), pp. 506–515.
- KDD-T-1999-KeimH #clustering #scalability #set
- Clustering Techniques for Large Data Sets — from the Past to the Future (DAK, AH), pp. 141–181.
- SIGMOD-1999-HinneburgK #clustering #database #scalability
- Clustering Methods for Large Databases: From the Past to the Future (AH, DAK), p. 509.
- VLDB-1999-KeimH #clustering #towards
- Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering (AH, DAK), pp. 506–517.
- KDD-1998-HinneburgK #approach #clustering #database #multi #performance #scalability
- An Efficient Approach to Clustering in Large Multimedia Databases with Noise (AH, DAK), pp. 58–65.