学術雑誌論文

学術雑誌論文

text

VLDB Journal. 2022, 31(4), p. 797-821

Fast, exact, and parallel-friendly outlier detection algorithms with proximity graph in metric spaces

Springer Science and Business Media Deutschland GmbH

英語

text

2022-07-01

https://doi.org/10.1007/s00778-022-00729-1

Amagata D., Onizuka M., Hara T.. Fast, exact, and parallel-friendly outlier detection algorithms with proximity graph in metric spaces. VLDB Journal 31, 797 (2022); https://doi.org/10.1007/s00778-022-00729-1.

In many fields, e.g., data mining and machine learning, distance-based outlier detection (DOD) is widely employed to remove noises and find abnormal phenomena, because DOD is unsupervised, can be employed in any metric spaces, and does not have any assumptions of data distributions. Nowadays, data mining and machine learning applications face the challenge of dealing with large datasets, which requires efficient DOD algorithms. We address the DOD problem with two different definitions. Our new idea, which solves the problems, is to exploit an in-memory proximity graph. For each problem, we propose a new algorithm that exploits a proximity graph and analyze an appropriate type of proximity graph for the algorithm. Our empirical study using real datasets confirms that our DOD algorithms are significantly faster than state-of-the-art ones.

This article is licensed under a Creative Commons Attribution 4.0 International License.

92778

Fast, exact, and parallel-friendly outlier detection algorithms with proximity graph in metric spaces

Springer Science and Business Media Deutschland GmbH

英語

2022-07-01

VLDB Journal

31

4

797

821

https://doi.org/10.1007/s00778-022-00729-1

https://hdl.handle.net/11094/92778