@inproceedings{aa6dcff48f2d47b28d532808ada9d971,
title = "Dimensional testing for multi-step similarity search",
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.",
keywords = "Adaptive similarity, Intrinsic dimensionality, Multi-step, Nearest neighbor, Similarity search, κ-NN",
author = "Houle, {Michael E.} and Xiguo Ma and Michael Nett and Vincent Oria",
year = "2012",
doi = "10.1109/ICDM.2012.91",
language = "English (US)",
isbn = "9780769549057",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
pages = "299--308",
booktitle = "Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012",
note = "12th IEEE International Conference on Data Mining, ICDM 2012 ; Conference date: 10-12-2012 Through 13-12-2012",
}