Adaptive similarity search in metric trees

Noha Yousri, Mohammed A. Ismail, Mohamed S. Kamel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Metric trees are designed for improving efficiency of similarity search in high dimensional data. Searching a metric tree always terminates at leaf nodes, restricting the hierarchical search to a certain leaf level, and the linear search in leafs to a particular leaf size. This may result in performing additional unneeded distance comparisons, which increases the search time as the number of dimensions increases. It is proposed that the search adapts itself to the query in question, so as to avoid unneeded distance comparisons. Thus, the hierarchical search should terminate when no more searchspace pruning can be useful, and search then continues linearly through the un-pruned data space. Hierarchical search can thus stop any where above or below the original leaf level of a metric tree. Search rules are proposed to adapt the search to the query parameters. A modification to the metric tree is also suggested to adopt the proposed rules. High dimensional gene expression data sets are used to evaluate the new algorithm, showing speed ups of 55% compared to traditional search.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
Pages419-424
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2007
Externally publishedYes
Event2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 - Montreal, QC, Canada
Duration: 7 Oct 200710 Oct 2007

Other

Other2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
CountryCanada
CityMontreal, QC
Period7/10/0710/10/07

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Yousri, N., Ismail, M. A., & Kamel, M. S. (2007). Adaptive similarity search in metric trees. In 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 (pp. 419-424). [4414022] https://doi.org/10.1109/ICSMC.2007.4414022