A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces.

Roger Weber, Hans-Jörg Schek, Stephen Blott: A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces. VLDB 1998: 194-205
  author    = {Roger Weber and
               Hans-J{\"o}rg Schek and
               Stephen Blott},
  editor    = {Ashish Gupta and
               Oded Shmueli and
               Jennifer Widom},
  title     = {A Quantitative Analysis and Performance Study for Similarity-Search
               Methods in High-Dimensional Spaces},
  booktitle = {VLDB'98, Proceedings of 24rd International Conference on Very
               Large Data Bases, August 24-27, 1998, New York City, New York,
  publisher = {Morgan Kaufmann},
  year      = {1998},
  isbn      = {1-55860-566-5},
  pages     = {194-205},
  ee        = {db/conf/vldb/WeberSB98.html},
  crossref  = {DBLP:conf/vldb/98},
  bibsource = {DBLP,}


For similarity search in high-dimensional vector spaces (or 'HDVSs'), researchers have proposed a number of new methods (or adaptations of existing methods) based, in the main, on data-space partitioning. However, the performance of these methods generally degrades as dimensionality increases. Although this phenomenon - known as the 'dimensional curse'- is well known, little or no quantitative analysis of the phenomenon is available. In this paper, we provide a detailed analysis of partitioning and clustering techniques for similarity search in HDVSs. We show formally that these methods exhibit linear complexity at high dimensionality, and that existing methods are outperformed on average by a simple sequential scan if the number of dimensions exceeds around 10. Consequently, we come up with an alternative organization based on approximations to make the unavoidable sequential scan as fast as possible. We describe a simple vector approximation scheme, called VA-file, and report on an experimental evaluation of this and of two tree-based index methods (an R*-tree and an X-tree).

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Ashish Gupta, Oded Shmueli, Jennifer Widom (Eds.): VLDB'98, Proceedings of 24rd International Conference on Very Large Data Bases, August 24-27, 1998, New York City, New York, USA. Morgan Kaufmann 1998, ISBN 1-55860-566-5
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Referenced by

  1. Stefan Berchtold, Christian Böhm, Daniel A. Keim, Florian Krebs, Hans-Peter Kriegel: On Optimizing Nearest Neighbor Queries in High-Dimensional Data Spaces. ICDT 2001: 435-449
  2. Charu C. Aggarwal, Alexander Hinneburg, Daniel A. Keim: On the Surprising Behavior of Distance Metrics in High Dimensional Spaces. ICDT 2001: 420-434
  3. Kaushik Chakrabarti, Sharad Mehrotra: Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces. VLDB 2000: 89-100
  4. Dimitrios Gunopulos, George Kollios, Vassilis J. Tsotras, Carlotta Domeniconi: Approximating Multi-Dimensional Aggregate Range Queries over Real Attributes. SIGMOD Conference 2000: 463-474
  5. Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jörg Sander: LOF: Identifying Density-Based Local Outliers. SIGMOD Conference 2000: 93-104
  6. Beng Chin Ooi, Kian-Lee Tan, Cui Yu, Stéphane Bressan: Indexing the Edges - A Simple and Yet Efficient Approach to High-Dimensional Indexing. PODS 2000: 166-174
  7. Roger Weber, Klemens Böhm: Trading Quality for Time with Nearest Neighbor Search. EDBT 2000: 21-35
  8. Christian Böhm, Hans-Peter Kriegel: Dynamically Optimizing High-Dimensional Index Structures. EDBT 2000: 36-50
  9. Aristides Gionis, Piotr Indyk, Rajeev Motwani: Similarity Search in High Dimensions via Hashing. VLDB 1999: 518-529
  10. Kaushik Chakrabarti, Sharad Mehrotra: The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces. ICDE 1999: 440-447
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