Work

Unsupervised Anomaly Detection Benchmark

Year: 2015

Type: dataset

Abstract: These datasets can be used for benchmarking unsupervised anomaly detection algorithms (for example "Local Outlier Factor" LOF). The datasets have been obtained from multiple sources and are mainly based on datasets originally used for supervised machine learning. By publishing these modifications, a comparison of different algorithms is now possible for unsupervised anomaly detection.

Source: Harvard Dataverse

DOI: 10.7910/dvn/opqmvf

Author: Goldstein, Markus

Institution: Kyushu University

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4398646337

Cites:

Cited by: 33

Related to: 10