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1999
ACM

Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering

9 years 1 months ago
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
Many applications require the clustering of large amounts of high-dimensional data. Most clustering algorithms, however, do not work e ectively and e ciently in highdimensional space, which is due to the so-called "curse of dimensionality". In addition, the high-dimensional data often contains a signi cant amount of noise which causes additional e ectiveness problems. In this paper, we review and compare the existing algorithms for clustering highdimensional data and show the impact of the curse of dimensionality on their e ectiveness and e ciency. The comparison reveals that condensation-based approaches such as BIRCH or STING are the most promising candidates for achieving the necessary e ciency, but it also shows that basically all condensation-based approaches have severe weaknesses with respect to their e ectiveness in highdimensional space. To overcome these problems, we develop a new clustering technique called OptiGrid which is based on constructing an optimal grid...
Alexander Hinneburg, Daniel A. Keim
Added 05 Aug 2010
Updated 05 Aug 2010
Type Conference
Year 1999
Where VLDB
Authors Alexander Hinneburg, Daniel A. Keim
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