A nonparametric outlier detection for effectively discovering top-N outliers from engineering data

Hongqin Fan; Osmar R. Zaïane; Andrew Foss; Junfeng Wu

A nonparametric outlier detection for effectively discovering top-N outliers from engineering data

Hongqin Fan, Osmar R. Zaïane, Andrew Foss, Junfeng Wu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

48 Citations (Scopus)

Abstract

We present a novel resolution-based outlier notion and a nonparametric outlier-mining algorithm, which can efficiently identify top listed outliers from a wide variety of datasets. The algorithm generates reasonable outlier results by taking both local and global features of a dataset into consideration. Experiments are conducted using both synthetic datasets and a real life construction equipment dataset from a large building contractor. Comparison with the current outlier mining algorithms indicates that the proposed algorithm is more effective.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
Pages	557-566
Number of pages	10
Publication status	Published - 14 Jul 2006
Externally published	Yes
Event	10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore Duration: 9 Apr 2006 → 12 Apr 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3918 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006
Country/Territory	Singapore
City	Singapore
Period	9/04/06 → 12/04/06

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Cite this

Fan, H., Zaïane, O. R., Foss, A., & Wu, J. (2006). A nonparametric outlier detection for effectively discovering top-N outliers from engineering data. In Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings (pp. 557-566). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3918 LNAI).

Fan, Hongqin ; Zaïane, Osmar R. ; Foss, Andrew et al. / A nonparametric outlier detection for effectively discovering top-N outliers from engineering data. Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings. 2006. pp. 557-566 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A nonparametric outlier detection for effectively discovering top-N outliers from engineering data",

abstract = "We present a novel resolution-based outlier notion and a nonparametric outlier-mining algorithm, which can efficiently identify top listed outliers from a wide variety of datasets. The algorithm generates reasonable outlier results by taking both local and global features of a dataset into consideration. Experiments are conducted using both synthetic datasets and a real life construction equipment dataset from a large building contractor. Comparison with the current outlier mining algorithms indicates that the proposed algorithm is more effective.",

author = "Hongqin Fan and Za{\"i}ane, {Osmar R.} and Andrew Foss and Junfeng Wu",

year = "2006",

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

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Fan, H, Zaïane, OR, Foss, A & Wu, J 2006, A nonparametric outlier detection for effectively discovering top-N outliers from engineering data. in Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3918 LNAI, pp. 557-566, 10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006, Singapore, Singapore, 9/04/06.

A nonparametric outlier detection for effectively discovering top-N outliers from engineering data. / Fan, Hongqin; Zaïane, Osmar R.; Foss, Andrew et al.
Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings. 2006. p. 557-566 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3918 LNAI).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - We present a novel resolution-based outlier notion and a nonparametric outlier-mining algorithm, which can efficiently identify top listed outliers from a wide variety of datasets. The algorithm generates reasonable outlier results by taking both local and global features of a dataset into consideration. Experiments are conducted using both synthetic datasets and a real life construction equipment dataset from a large building contractor. Comparison with the current outlier mining algorithms indicates that the proposed algorithm is more effective.

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Fan H, Zaïane OR, Foss A, Wu J. A nonparametric outlier detection for effectively discovering top-N outliers from engineering data. In Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings. 2006. p. 557-566. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

A nonparametric outlier detection for effectively discovering top-N outliers from engineering data

Abstract

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ASJC Scopus subject areas

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