Pivot Selection Methods Based on Covariance and Correlation for Metric-space Indexing
Authors
Kewei Ma, Yuanjun Liu, Honglong Xu, Pang Yue, Fuli Lei, Sheng Liu, Rui Mao, Jiaxin Han
Corresponding Author
Kewei Ma
Available Online November 2012.
- DOI
- 10.2991/citcs.2012.258How to use a DOI?
- Keywords
- similarity query; metric-space indexing; pivot space model; pivot selection;
- Abstract
Metric-space indexing is a general method for similarity queries of complex data. The quality of the index tree is a critical factor of the query performance. Bulkloading a metricspace indexing tree can be represented by two recursive steps, pivot selection and data partition, while pivot selection dominants the quality of the index tree. Two heuristics, based on covariance and correlation, for pivot selection are proposed. Empirical results show that their performance is superior or comparable to existing methods.
- Copyright
- © 2012, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Kewei Ma AU - Yuanjun Liu AU - Honglong Xu AU - Pang Yue AU - Fuli Lei AU - Sheng Liu AU - Rui Mao AU - Jiaxin Han PY - 2012/11 DA - 2012/11 TI - Pivot Selection Methods Based on Covariance and Correlation for Metric-space Indexing BT - Proceedings of the 2012 National Conference on Information Technology and Computer Science PB - Atlantis Press SP - 1015 EP - 1020 SN - 1951-6851 UR - https://doi.org/10.2991/citcs.2012.258 DO - 10.2991/citcs.2012.258 ID - Ma2012/11 ER -