@inproceedings{Hinneburg1999Optim-5790,
  year={1999},
  title={Optimal Grid-Clustering : Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering},
  booktitle={Proceedings of the 25 th International Conference on Very Large Databases, 1999},
  pages={506--517},
  author={Hinneburg, Alexander and Keim, Daniel A.}
}

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    <dc:creator>Keim, Daniel A.</dc:creator>
    <dcterms:abstract xml:lang="eng">Many applications require the clustering of large amounts of high-dimensional data. Most clustering algorithms, however, do not work effectively and efficiently in high-dimensional space, which is due to the so-called "curse of dimensionality". In addition, the high-dimensional data often contains a significant amount of noise which causes additional effectiveness problems. In this paper, we review and compare the existing algorithms for clustering high-dimensional data and show the impact of the curse of dimensionality on their effectiveness and efficiency. The  comparison  reveals that condensation-based approaches (such as BIRCH or STING) are the most promising candidates for achieving the necessary efficiency, but it also shows  that basically all condensation-based approaches have severe weaknesses with respect to their effectiveness in high-dimensional space. To overcome these problems, we develop a new clustering technique called OptiGrid which is based on constructing an optimal grid-partitioning of the data. The optimal grid-partitioning is determined by calculating the best partitioning hyperplanes for each dimension (if such a partitioning exists) using certain projections of the data. The advantages of our new approach are (1) it has a firm mathematical basis (2) it is by far more effective than existing clustering algorithms for high-dimensional data (3) it is very efficient even for large data sets of high dimensionality. To demonstrate the effectiveness and efficiency of our new approach, we perform a series of experiments on a number of different data sets including real data sets from CAD and molecular biology. A comparison with one of the best known algorithms (BIRCH) shows the superiority of our new approach.</dcterms:abstract>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
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    <dcterms:issued>1999</dcterms:issued>
    <dcterms:bibliographicCitation>First publ. in: Proceedings of the 25th International Conference on Very Large Databases, 1999, pp. 506-517</dcterms:bibliographicCitation>
    <dcterms:title>Optimal Grid-Clustering : Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering</dcterms:title>
    <dc:creator>Hinneburg, Alexander</dc:creator>
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