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Brief Application Description; Visual Data Mining: Recognizing Telephone Calling Fraud

Authors:

Kenneth C. Cox,

Stephen G. Eick,

Graham J. Wills,

Ronald J. BrachmanAuthors Info & Claims

Data Mining and Knowledge Discovery, Volume 1, Issue 2

Pages 225 - 231

https://doi.org/10.1023/A:1009740009307

Published: 21 January 1997 Publication History

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Abstract

Human pattern recognition skills are remarkable and in many situations far exceed the ability of automated mining algorithms. By building domain-specific interfaces that present information visually, we can combine human detection with machines‘ far greater computational capacity. We illustrate our ideas by describing a suite of visual interfaces we built for telephone fraud detection.

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Cao SYang XChen CZhou JLi XQi Y(2019)TitAntProceedings of the VLDB Endowment10.14778/3352063.335212612:12(2082-2093)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.14778/3352063.3352126
Subudhi SPanigrahi SBehera T(2016)Detection of Mobile Phone Fraud Using Possibilistic Fuzzy C-Means Clustering and Hidden Markov ModelInternational Journal of Synthetic Emotions10.4018/IJSE.20160701027:2(23-44)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.4018/IJSE.2016070102
Subudhi SPanigrahi S(2016)Use of fuzzy clustering and support vector machine for detecting fraud in mobile telecommunication networksInternational Journal of Security and Networks10.1504/IJSN.2016.07506911:1/2(3-11)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1504/IJSN.2016.075069
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Brief Application Description; Visual Data Mining: Recognizing Telephone Calling Fraud

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cover image Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery Volume 1, Issue 2

1997

93 pages

ISSN:1384-5810

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Kluwer Academic Publishers

United States

Publication History

Published: 21 January 1997

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Cao SYang XChen CZhou JLi XQi Y(2019)TitAntProceedings of the VLDB Endowment10.14778/3352063.335212612:12(2082-2093)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.14778/3352063.3352126
Subudhi SPanigrahi SBehera T(2016)Detection of Mobile Phone Fraud Using Possibilistic Fuzzy C-Means Clustering and Hidden Markov ModelInternational Journal of Synthetic Emotions10.4018/IJSE.20160701027:2(23-44)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.4018/IJSE.2016070102
Subudhi SPanigrahi S(2016)Use of fuzzy clustering and support vector machine for detecting fraud in mobile telecommunication networksInternational Journal of Security and Networks10.1504/IJSN.2016.07506911:1/2(3-11)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1504/IJSN.2016.075069
Abdallah AMaarof MZainal A(2016)Fraud detection systemJournal of Network and Computer Applications10.1016/j.jnca.2016.04.00768:C(90-113)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.jnca.2016.04.007
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Moudani WZaarour GMora-Camino F(2014)Fuzzy Classification of Customer Insolvency in Mobile TelecommunicationInternational Journal of Decision Support System Technology10.4018/ijdsst.20140701016:3(1-29)Online publication date: 1-Jul-2014
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Polese G(2014)A decision support system for Evidence Based MedicineJournal of Visual Languages and Computing10.1016/j.jvlc.2014.09.01325:6(858-867)Online publication date: 1-Dec-2014
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Azevedo ASantos M(2013)Extending QMBE Language with ClusteringInternational Journal of Decision Support System Technology10.4018/ijdsst.20131001045:4(59-77)Online publication date: 1-Oct-2013
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Wei WLi JCao LOu YChen J(2013)Effective detection of sophisticated online banking fraud on extremely imbalanced dataWorld Wide Web10.1007/s11280-012-0178-016:4(449-475)Online publication date: 1-Jul-2013
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Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
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A Web-Based Visual Spatial Co-Location Patterns' Mining Prototype System (SCPMiner)

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