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Ju-hong Lee, Deok‐Hwan Kim, C. Chung (1999)
Multi-dimensional selectivity estimation using compressed histogram information
Yi-Leh Wu, D. Agrawal, A. Abbadi (2000)
A comparison of DFT and DWT based similarity search in time-series databases
C. Aggarwal, Alexander Hinneburg, D. Keim (2001)
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
J. Vitter, Min Wang (1999)
Approximate computation of multidimensional aggregates of sparse data using wavelets
K. Chakrabarti, S. Mehrotra (1999)
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Hui Jin, B. Ooi, Heng Shen, Cui Yu, Aoying Zhou (2003)
An adaptive and efficient dimensionality reduction algorithm for high-dimensional indexingProceedings 19th International Conference on Data Engineering (Cat. No.03CH37405)
Cui Yu, B. Ooi, K. Tan, H. Jagadish (2001)
Indexing the Distance: An Efficient Method to KNN Processing
Stefan Berchtold, Christian Böhm, H. Kriegel (1998)
The pyramid-technique: towards breaking the curse of dimensionality
I.T. Jolliffe (1986)
Principal Component Analysis
B. Ooi, K. Tan, Cui Yu, S. Bressan (2000)
Indexing the edges—a simple and yet efficient approach to high-dimensional indexing
Yasushi Sakurai, Masatoshi Yoshikawa, Shunsuke Uemura, Haruhiko Kojima (2000)
The A-tree: An Index Structure for High-Dimensional Spaces Using Relative Approximation
C. Böhm, Stefan Berchtold, D. Keim (2001)
Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databasesACM Comput. Surv., 33
C. Aggarwal, Cecilia Procopiuc, J. Wolf, Philip Yu, Jong Park (1999)
Fast algorithms for projected clustering
Alexander Hinneburg, D. Keim (1999)
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
James Wang, G. Wiederhold, O. Firschein, S. Wei (1998)
Content-based image indexing and searching using Daubechies' waveletsInternational Journal on Digital Libraries, 1
J.Z. Wang, G. Wiederhold, O. Firschein, S.X. Wei (1998)
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K. Chakrabarti, S. Mehrotra (2000)
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
R. Weber, H. Schek, Stephen Blott (1998)
A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces
K. Beyer, J. Goldstein, R. Ramakrishnan, U. Shaft (1999)
When Is ''Nearest Neighbor'' Meaningful?
The notorious “dimensionality curse” is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B + -tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently.
The VLDB Journal – Springer Journals
Published: Apr 1, 2007
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