Article

A fast greedy algorithm for outlier mining

Authors:

Shengchun Deng,

Joshua Zhexue HuangAuthors Info & Claims

PAKDD'06: Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining

Pages 567 - 576

https://doi.org/10.1007/11731139_67

Published: 09 April 2006 Publication History

Abstract

The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Recently, the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very time-consuming on very large datasets. In this paper, we present a very fast greedy algorithm for mining outliers under the same optimization model. Experimental results on real datasets and large synthetic datasets show that: (1) Our new algorithm has comparable performance with respect to those state-of-the-art outlier detection algorithms on identifying true outliers and (2) Our algorithm can be an order of magnitude faster than LSA algorithm.

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Cited By

Taha AHadi A(2019)Anomaly Detection Methods for Categorical DataACM Computing Surveys10.1145/331273952:2(1-35)Online publication date: 30-May-2019
https://dl.acm.org/doi/10.1145/3312739
Nakjai PKatanyukul T(2018)Automatic Hand Sign Recognition: Identify Unusuality Through Latent CognizanceArtificial Neural Networks in Pattern Recognition10.1007/978-3-319-99978-4_20(255-267)Online publication date: 19-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-319-99978-4_20
Do TTran TLe XDuong T(2017)Detecting Special Lecturers Using Information theory-based Outlier Detection MethodProceedings of the International Conference on Compute and Data Analysis10.1145/3093241.3093274(240-244)Online publication date: 19-May-2017
https://dl.acm.org/doi/10.1145/3093241.3093274
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Information & Contributors

Information

Published In

cover image Guide Proceedings

PAKDD'06: Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining

April 2006

876 pages

ISBN:3540332065

Editors:
Wee-Keong Ng
Nanyang Technological University, Singapore
,
Masaru Kitsuregawa
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan
,
Jianzhong Li
School of Computer Science and Technology, Heilongjiang University, 4-6-1 Komaba, Meguro-ku, Tokyo, China
,
Kuiyu Chang
School of Computer Engineering, Nanyang Technological University, 4-6-1 Komaba, Meguro-ku, Singapore, Singapore

Sponsors

SAS
Air Force Office of Scientific Research/Asian Office of Aerospace R&D
Lee Foundation
US Army ITC-PAC Asian Research Office
Infocomm Development Authority of Singapore: Infocomm Development Authority of Singapore

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 09 April 2006

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Cited By

Taha AHadi A(2019)Anomaly Detection Methods for Categorical DataACM Computing Surveys10.1145/331273952:2(1-35)Online publication date: 30-May-2019
https://dl.acm.org/doi/10.1145/3312739
Nakjai PKatanyukul T(2018)Automatic Hand Sign Recognition: Identify Unusuality Through Latent CognizanceArtificial Neural Networks in Pattern Recognition10.1007/978-3-319-99978-4_20(255-267)Online publication date: 19-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-319-99978-4_20
Do TTran TLe XDuong T(2017)Detecting Special Lecturers Using Information theory-based Outlier Detection MethodProceedings of the International Conference on Compute and Data Analysis10.1145/3093241.3093274(240-244)Online publication date: 19-May-2017
https://dl.acm.org/doi/10.1145/3093241.3093274
Kao LHuang YSandnes F(2015)Associating absent frequent itemsets with infrequent items to identify abnormal transactionsApplied Intelligence10.1007/s10489-014-0622-142:4(694-706)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s10489-014-0622-1
Suri NMurty MAthithan G(2014)A ranking-based algorithm for detection of outliers in categorical dataInternational Journal of Hybrid Intelligent Systems10.3233/HIS-13017911:1(1-11)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.3233/HIS-130179
Koufakou AGeorgiopoulos M(2010)A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributesData Mining and Knowledge Discovery10.1007/s10618-009-0148-z20:2(259-289)Online publication date: 1-Mar-2010
https://dl.acm.org/doi/10.1007/s10618-009-0148-z
Shaari FBakar AHamdan A(2009)Outlier detection based on rough sets theoryIntelligent Data Analysis10.5555/1551582.155158413:2(191-206)Online publication date: 1-Apr-2009
https://dl.acm.org/doi/10.5555/1551582.1551584
Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
https://dl.acm.org/doi/10.1145/1541880.1541882

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