Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TKDE.2021.3058241 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | DocType | Volume |
Cumulative distance distribution,error-bound guaranteed approximation,lower and upper bounds | Journal | 34 |
Issue | ISSN | Citations |
11 | 1041-4347 | 0 |
PageRank | References | Authors |
0.34 | 23 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiahao Zhang | 1 | 0 | 0.34 |
| man lung yiu | 2 | 2436 | 109.78 |
| Bo Tang | 3 | 25 | 8.85 |
| Qing Li | 4 | 3222 | 433.87 |