Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?
- Michael E. HouleAffiliated withNational Institute of Informatics
- , Hans-Peter KriegelAffiliated withLudwig-Maximilians-Universität München
- , Peer KrögerAffiliated withLudwig-Maximilians-Universität München
- , Erich SchubertAffiliated withLudwig-Maximilians-Universität München
- , Arthur ZimekAffiliated withLudwig-Maximilians-Universität München
The performance of similarity measures for search, indexing, and data mining applications tends to degrade rapidly as the dimensionality of the data increases. The effects of the so-called ‘curse of dimensionality’ have been studied by researchers for data sets generated according to a single data distribution. In this paper, we study the effects of this phenomenon on different similarity measures for multiply-distributed data. In particular, we assess the performance of shared-neighbor similarity measures, which are secondary similarity measures based on the rankings of data objects induced by some primary distance measure. We find that rank-based similarity measures can result in more stable performance than their associated primary distance measures.
- Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?
- Book Title
- Scientific and Statistical Database Management
- Book Subtitle
- 22nd International Conference, SSDBM 2010, Heidelberg, Germany, June 30–July 2, 2010. Proceedings
- pp 482-500
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 16. Institute of Computer Science, University of Heidelberg
- 17. Dept. of Computer Science and Genome Center, University of California
- Author Affiliations
- 18. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan
- 19. Ludwig-Maximilians-Universität München, Oettingenstr. 67, 80538, München, Germany
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