Dimensional testing for multi-step similarity search

Document Type

Conference Proceeding

Publication Date

12-1-2012

Abstract

In data mining applications such as subspace clustering or feature selection, changes to the underlying feature set can require the reconstruction of search indices to support fundamental data mining tasks. For such situations, multi-step search approaches have been proposed that can accommodate changes in the underlying similarity measure without the need to rebuild the index. In this paper, we present a heuristic multi-step search algorithm that utilizes a measure of intrinsic dimension, the generalized expansion dimension (GED), as the basis of its search termination condition. Compared to the current state-of-the-art method, experimental results show that our heuristic approach is able to obtain significant improvements in both the number of candidates and the running time, while losing very little in the accuracy of the query results. © 2012 IEEE.

Identifier

84874079752 (Scopus)

ISBN

[9780769549057]

Publication Title

Proceedings IEEE International Conference on Data Mining Icdm

External Full Text Location

https://doi.org/10.1109/ICDM.2012.91

ISSN

15504786

First Page

299

Last Page

308

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