A unified subspace outlier ensemble framework for outlier detection

Zengyou He; Shengchun Deng; Xiaofei Xu

doi:10.1007/11563952_56

A unified subspace outlier ensemble framework for outlier detection

Zengyou He^*
, Shengchun Deng
, Xiaofei Xu

^*Corresponding author for this work

Faculty of Computing

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

This paper proposes a unified framework for outlier detection in high dimensional spaces from an ensemble-learning viewpoint. Moreover, to demonstrate the usefulness of our framework, we developed a very simple and fast algorithm, namely SOE1, in which only subspaces with one dimension is used for mining outliers from large categorical datasets. Experimental results demonstrate the superiority of SOE1 algorithm.

Original language	English
Title of host publication	Advances in Web-Age Information Management - 6th International Conference, WAIM 2005, Proceedings
Pages	632-637
Number of pages	6
DOIs	https://doi.org/10.1007/11563952_56
State	Published - 2005
Event	6th International Conference on Advances in Web-Age Information Management, WAIM 2005 - Hangzhou, China Duration: 11 Oct 2005 → 13 Oct 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3739 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Conference on Advances in Web-Age Information Management, WAIM 2005
Country/Territory	China
City	Hangzhou
Period	11/10/05 → 13/10/05

Access to Document

10.1007/11563952_56

Cite this

He, Z., Deng, S., & Xu, X. (2005). A unified subspace outlier ensemble framework for outlier detection. In Advances in Web-Age Information Management - 6th International Conference, WAIM 2005, Proceedings (pp. 632-637). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3739 LNCS). https://doi.org/10.1007/11563952_56

@inproceedings{1226c447116a46909b346fa7bcb35b83,

title = "A unified subspace outlier ensemble framework for outlier detection",

abstract = "This paper proposes a unified framework for outlier detection in high dimensional spaces from an ensemble-learning viewpoint. Moreover, to demonstrate the usefulness of our framework, we developed a very simple and fast algorithm, namely SOE1, in which only subspaces with one dimension is used for mining outliers from large categorical datasets. Experimental results demonstrate the superiority of SOE1 algorithm.",

author = "Zengyou He and Shengchun Deng and Xiaofei Xu",

year = "2005",

doi = "10.1007/11563952\_56",

language = "英语",

isbn = "3540292276",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "632--637",

booktitle = "Advances in Web-Age Information Management - 6th International Conference, WAIM 2005, Proceedings",

note = "6th International Conference on Advances in Web-Age Information Management, WAIM 2005 ; Conference date: 11-10-2005 Through 13-10-2005",

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He, Z, Deng, S & Xu, X 2005, A unified subspace outlier ensemble framework for outlier detection. in Advances in Web-Age Information Management - 6th International Conference, WAIM 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3739 LNCS, pp. 632-637, 6th International Conference on Advances in Web-Age Information Management, WAIM 2005, Hangzhou, China, 11/10/05. https://doi.org/10.1007/11563952_56

A unified subspace outlier ensemble framework for outlier detection. / He, Zengyou; Deng, Shengchun; Xu, Xiaofei.
Advances in Web-Age Information Management - 6th International Conference, WAIM 2005, Proceedings. 2005. p. 632-637 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3739 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AB - This paper proposes a unified framework for outlier detection in high dimensional spaces from an ensemble-learning viewpoint. Moreover, to demonstrate the usefulness of our framework, we developed a very simple and fast algorithm, namely SOE1, in which only subspaces with one dimension is used for mining outliers from large categorical datasets. Experimental results demonstrate the superiority of SOE1 algorithm.

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A unified subspace outlier ensemble framework for outlier detection

Abstract

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Conference

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