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 proceedingConference article published in proceeding or bookAcademic researchpeer-review

42 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 languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
Pages557-566
Number of pages10
Publication statusPublished - 14 Jul 2006
Externally publishedYes
Event10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
Duration: 9 Apr 200612 Apr 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3918 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006
CountrySingapore
CitySingapore
Period9/04/0612/04/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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