Searching Density-Increasing Path to Local Density Peaks for Unsupervised Anomaly Detection-Reference-Cited by-同舟云学术

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Searching Density-Increasing Path to Local Density Peaks for Unsupervised Anomaly Detection

Published:2023-08-01 Issue:4 Volume:9 Page:1198-1209
ISSN:2332-7790
Container-title:IEEE Transactions on Big Data
language:
Short-container-title:IEEE Trans. Big Data

Author:

Zhao Jiachen¹^ORCID,Deng Fang²^ORCID,Zhu Jiaqi²^ORCID,Chen Jie³^ORCID

Affiliation:

1. National Research Center for Information Science and Technology, Tsinghua University, Beijing, China

2. Department of Automaion, Beijing Institute of Technology, Beijing, China

3. State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, China

Funder

National Natural Science Foundation of China

National Science Fund for Distinguished Young Scholars of China

China Postdoctoral Science Foundation

Shuimu Tsinghua Scholar Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Information Systems and Management,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/6687317/10177823/10103526.pdf?arnumber=10103526

Reference45 articles.

1. Tabular Data Anomaly Detection Based on Density Peak Clustering Algorithm

2. Density peak clustering based on relative density relationship

3. Hyperspectral anomaly detection via density peak clustering

4. CciMST: A Clustering Algorithm Based on Minimum Spanning Tree and Cluster Centers

5. Online and Unsupervised Anomaly Detection for Streaming Data Using an Array of Sliding Windows and PDDs

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1. An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection;Applied Sciences;2023-04-30

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