Hephaistos: A fast and distributed outlier detection approach for big mixed attribute data-Reference-Cited by-同舟云学术

Hephaistos: A fast and distributed outlier detection approach for big mixed attribute data

Published:2019-09-26 Issue:4 Volume:23 Page:759-778
ISSN:1088-467X
Container-title:Intelligent Data Analysis
language:
Short-container-title:IDA

Author:

Du Haizhou¹,Fang Wei¹,Wang Yi²

Affiliation:

1. School of Computer Science, Shanghai University of Electric Power, Shanghai, China

2. State Grid Zhejiang Hangzhou Xiaoshan Power Supply Company, Hangzhou, Zhejiang, China

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science

Reference28 articles.

1. Outlier detection for high dimensional data;Aggarwal;Acm Sigmod Record,2001

2. An efficient algorithm for distributed density-based outlier detection on big data;Bai;Neurocomputing,2016

3. Bhaduri, Algorithms for speeding up distance-based outlier detection, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011, pp. 859–867.

4. Breunig, Lof: identifying density-based local outliers, Acm Sigmod Record 29(2) (2000), 93–104.

5. K. Chen, The “best k” for entropy-based categorical data clustering, in: International Conference on Scientific and Statistical Database Management, SSDBM 2005, 27–29 June 2005, University of California, Santa Barbara, Ca, Usa, Proceedings, 2005, pp. 253–262.

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Local outlier factor for anomaly detection in HPCC systems;Journal of Parallel and Distributed Computing;2024-10

2. Information granularity-based incremental feature selection for partially labeled hybrid data;Intelligent Data Analysis;2022-01-13

3. Optimisations of four imputation frameworks for performance exploring based on decision tree algorithms in big data analysis problems;International Journal of Computational Science and Engineering;2022