Title :
Using projections to visually cluster high-dimensional data
Author :
Hinneburg, Alexander ; Keim, Daniel ; Wawryniuk, Markus
Author_Institution :
Halle-Wittenberg Univ., Germany
Abstract :
The High-Dimensional Eye system proves that a tight integration of advanced clustering algorithms and state-of-the-art visualization techniques can help us better understand and effectively guide the clustering process, and thus significantly improve the clustering results.
Keywords :
data mining; data visualisation; data warehouses; pattern clustering; High-Dimensional Eye system; clustering algorithms; projections; visual high dimensional data clustering; visualization techniques; Application software; Clustering algorithms; Data mining; Data visualization; Machine learning; Multidimensional systems; Particle separators; Partitioning algorithms; Statistics; Visual databases;
Journal_Title :
Computing in Science & Engineering
DOI :
10.1109/MCISE.2003.1182958
