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» Subspace Clustering of High Dimensional Data
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VLDB
2007
ACM
174views Database» more  VLDB 2007»
15 years 10 months ago
An adaptive and dynamic dimensionality reduction method for high-dimensional indexing
Abstract The notorious "dimensionality curse" is a wellknown phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approa...
Heng Tao Shen, Xiaofang Zhou, Aoying Zhou
135
Voted
SIGMOD
2002
ACM
246views Database» more  SIGMOD 2002»
15 years 10 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 suc...
Charu C. Aggarwal
PAKDD
2009
ACM
153views Data Mining» more  PAKDD 2009»
15 years 5 months ago
Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data
Hans-Peter Kriegel, Peer Kröger, Erich Schube...
SSDBM
2006
IEEE
123views Database» more  SSDBM 2006»
15 years 4 months ago
Mining Hierarchies of Correlation Clusters
The detection of correlations between different features in high dimensional data sets is a very important data mining task. These correlations can be arbitrarily complex: One or...
Elke Achtert, Christian Böhm, Peer Kröge...
83
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SODA
2010
ACM
171views Algorithms» more  SODA 2010»
15 years 7 months ago
Coresets and Sketches for High Dimensional Subspace Approximation Problems
We consider the problem of approximating a set P of n points in Rd by a j-dimensional subspace under the p measure, in which we wish to minimize the sum of p distances from each p...
Dan Feldman, Morteza Monemizadeh, Christian Sohler...