Article

Outlier detection by sampling with accuracy guarantees

Authors:

Christopher JermaineAuthors Info & Claims

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 767 - 772

https://doi.org/10.1145/1150402.1150501

Published: 20 August 2006 Publication History

Abstract

An effective approach to detecting anomalous points in a data set is distance-based outlier detection. This paper describes a simple sampling algorithm to effciently detect distance-based outliers in domains where each and every distance computation is very expensive. Unlike any existing algorithms, the sampling algorithm requires a xed number of distance computations and can return good results with accuracy guarantees. The most computationally expensive aspect of estimating the accuracy of the result is sorting all of the distances computed by the sampling algorithm. The experimental study on two expensive domains as well as ten additional real-life datasets demonstrates both the effciency and effectiveness of the sampling algorithm in comparison with the state-of-the-art algorithm and there liability of the accuracy guarantees.

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Cited By

Muhr DAffenzeller MKüng J(2023)A Probabilistic Transformation of Distance-Based OutliersMachine Learning and Knowledge Extraction10.3390/make50300425:3(782-802)Online publication date: 18-Jul-2023
https://doi.org/10.3390/make5030042
Luo Z(2022)Determinants of the perceived usefulness (PU) in the context of using gamification for classroom-based ESL teaching: A scale development studyEducation and Information Technologies10.1007/s10639-022-11409-628:4(4741-4768)Online publication date: 24-Oct-2022
https://dl.acm.org/doi/10.1007/s10639-022-11409-6
Elghamrawy SLotfy MElawady Y(2022)An Intrusion Detection Model Based on Deep Learning and Multi-layer Perceptron in the Internet of Things (IoT) NetworkThe 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022)10.1007/978-3-031-03918-8_4(34-46)Online publication date: 17-Apr-2022
https://doi.org/10.1007/978-3-031-03918-8_4
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Index Terms

Outlier detection by sampling with accuracy guarantees
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2006

986 pages

ISBN:1595933395

DOI:10.1145/1150402

Conference Chair:
Tina Eliassi-Rad
LLNL
,
General Chair:
Lyle Ungar
University of Pennsylvania
,
Program Chairs:
Mark Craven
University of Wisconsin
,
Dimitrios Gunopulos
University of California, Riverside

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 20 August 2006

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KDD06: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 20 - 23, 2006

PA, Philadelphia, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Muhr DAffenzeller MKüng J(2023)A Probabilistic Transformation of Distance-Based OutliersMachine Learning and Knowledge Extraction10.3390/make50300425:3(782-802)Online publication date: 18-Jul-2023
https://doi.org/10.3390/make5030042
Luo Z(2022)Determinants of the perceived usefulness (PU) in the context of using gamification for classroom-based ESL teaching: A scale development studyEducation and Information Technologies10.1007/s10639-022-11409-628:4(4741-4768)Online publication date: 24-Oct-2022
https://dl.acm.org/doi/10.1007/s10639-022-11409-6
Elghamrawy SLotfy MElawady Y(2022)An Intrusion Detection Model Based on Deep Learning and Multi-layer Perceptron in the Internet of Things (IoT) NetworkThe 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022)10.1007/978-3-031-03918-8_4(34-46)Online publication date: 17-Apr-2022
https://doi.org/10.1007/978-3-031-03918-8_4
Naghavi Nozad SAmir Haeri MFolino G(2021)SDCORKnowledge-Based Systems10.1016/j.knosys.2021.107256228:COnline publication date: 23-Aug-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.107256
Pang GCao L(2020)Heterogeneous Univariate Outlier Ensembles in Multidimensional DataACM Transactions on Knowledge Discovery from Data10.1145/340393414:6(1-27)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3403934
Boukerche AZheng LAlfandi O(2020)Outlier DetectionACM Computing Surveys10.1145/338102853:3(1-37)Online publication date: 12-Jun-2020
https://dl.acm.org/doi/10.1145/3381028
Le KPapotti P(2020)User-driven Error Detection for Time Series with Events2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00070(745-757)Online publication date: Apr-2020
https://doi.org/10.1109/ICDE48307.2020.00070
Vanhoeyveld JMartens DPeeters B(2020)Value-added tax fraud detection with scalable anomaly detection techniquesApplied Soft Computing10.1016/j.asoc.2019.10589586:COnline publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1016/j.asoc.2019.105895
Kerner HWagstaff KBue BWellington DJacob SHorton PBell JKwan CBen Amor H(2020)Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missionsData Mining and Knowledge Discovery10.1007/s10618-020-00697-6Online publication date: 16-Jun-2020
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Wang LShi LXu LLiu PZhang LDong Y(2019)A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization MethodInformation10.3390/info1101002611:1(26)Online publication date: 31-Dec-2019
https://doi.org/10.3390/info11010026
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