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Fast Error-Bounded Distance Distribution Computation | IEEE Journals & Magazine | IEEE Xplore

Fast Error-Bounded Distance Distribution Computation


Abstract:

In this work we study the distance distribution computation problem. It has been widely used in many real-world applications, e.g., human genome clustering, cosmological ...Show More

Abstract:

In this work we study the distance distribution computation problem. It has been widely used in many real-world applications, e.g., human genome clustering, cosmological model analysis, and parameter tuning. The straightforward solution for the exact distance distribution computation problem is unacceptably slow due to (i) massive data size, and (ii) expensive distance computation. In this paper, we propose a novel method to compute approximate distance distributions with error bound guarantees. Furthermore, our method is generic to different distance measures. We conduct extensive experimental studies on three widely used distance measures with real-world datasets. The experimental results demonstrate that our proposed method outperforms the sampling-based solution (without error guarantees) by up to three orders of magnitude.
Published in: IEEE Transactions on Knowledge and Data Engineering ( Volume: 34, Issue: 11, 01 November 2022)
Page(s): 5364 - 5377
Date of Publication: 09 February 2021

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