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SIGMOD
2002
ACM

Hierarchical subspace sampling: a unified framework for high dimensional data reduction, selectivity estimation and nearest neig

9 years 11 months ago
Hierarchical subspace sampling: a unified framework for high dimensional data reduction, selectivity estimation and nearest neig
With the increased abilities for automated data collection made possible by modern technology, the typical sizes of data collections have continued to grow in recent years. In such cases, it may be desirable to store the data in a reduced format in order to improve the storage, transfer time, and processing requirements on the data. One of the challenges of designing e ective data compression techniques is to be able to preserve the ability to use the reduced format directly for a wide range of database and data mining applications. In this paper, we propose the novel idea of hierarchical subspace sampling in order to create a reduced representation of the data. The method is naturally able to estimate the local implicit dimensionalities of each point very e ectively, and thereby create a variable dimensionality reduced representation of the data. Such a technique has the advantage that it is very adaptive about adjusting its representation depending upon the behavior of the immediate...
Charu C. Aggarwal
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2002
Where SIGMOD
Authors Charu C. Aggarwal
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