Finding interesting outliers - A Belief Network based approach

Adnan Masood; Wei Li

doi:10.1109/SECON.2015.7132918

Finding interesting outliers - A Belief Network based approach

Adnan Masood
, Wei Li

Nova Southeastern University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Outliers are deviations from the usual trends of data; to discover interestingness among outliers i.e. finding anomalies which are of real-interest for subject matter experts is an active area of research in data mining and machine learning community. Due to its subjective nature, the definition of what amounts to 'interesting' varies between domains and subject matter experts. This paper provides an overview of the current state of quantification for measures of interestingness, using Bayesian Belief Networks as background knowledge. Building up on this foundation, we also provide a process flow for ranking outliers based on subject matter expert's apriori interestingness.

Original language	English
Title of host publication	IEEE SoutheastCon 2015 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Edition	June
ISBN (Electronic)	9781467373005
ISBN (Print)	9781467373005
DOIs	https://doi.org/10.1109/SECON.2015.7132918
State	Published - Jun 24 2015
Event	IEEE SoutheastCon 2015 - Fort Lauderdale, United States Duration: Apr 9 2015 → Apr 12 2015

Publication series

Name	SoutheastCon 2015

Conference

Conference	IEEE SoutheastCon 2015
Country/Territory	United States
City	Fort Lauderdale
Period	4/9/15 → 4/12/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

ASJC Scopus Subject Areas

Computer Networks and Communications
Software
Electrical and Electronic Engineering
Control and Systems Engineering
Signal Processing

Keywords

Bayesian Belief Network
Interestingness Measures
Outlier Analysis
Probabilistic Graphical Models

Access to Document

10.1109/SECON.2015.7132918

Cite this

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title = "Finding interesting outliers - A Belief Network based approach",

abstract = "Outliers are deviations from the usual trends of data; to discover interestingness among outliers i.e. finding anomalies which are of real-interest for subject matter experts is an active area of research in data mining and machine learning community. Due to its subjective nature, the definition of what amounts to 'interesting' varies between domains and subject matter experts. This paper provides an overview of the current state of quantification for measures of interestingness, using Bayesian Belief Networks as background knowledge. Building up on this foundation, we also provide a process flow for ranking outliers based on subject matter expert's apriori interestingness. ",

keywords = "Bayesian Belief Network, Interestingness Measures, Outlier Analysis, Probabilistic Graphical Models",

author = "Adnan Masood and Wei Li",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE SoutheastCon 2015 ; Conference date: 09-04-2015 Through 12-04-2015",

year = "2015",

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doi = "10.1109/SECON.2015.7132918",

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T1 - Finding interesting outliers - A Belief Network based approach

AU - Masood, Adnan

AU - Li, Wei

PY - 2015/6/24

Y1 - 2015/6/24

N2 - Outliers are deviations from the usual trends of data; to discover interestingness among outliers i.e. finding anomalies which are of real-interest for subject matter experts is an active area of research in data mining and machine learning community. Due to its subjective nature, the definition of what amounts to 'interesting' varies between domains and subject matter experts. This paper provides an overview of the current state of quantification for measures of interestingness, using Bayesian Belief Networks as background knowledge. Building up on this foundation, we also provide a process flow for ranking outliers based on subject matter expert's apriori interestingness.

AB - Outliers are deviations from the usual trends of data; to discover interestingness among outliers i.e. finding anomalies which are of real-interest for subject matter experts is an active area of research in data mining and machine learning community. Due to its subjective nature, the definition of what amounts to 'interesting' varies between domains and subject matter experts. This paper provides an overview of the current state of quantification for measures of interestingness, using Bayesian Belief Networks as background knowledge. Building up on this foundation, we also provide a process flow for ranking outliers based on subject matter expert's apriori interestingness.

KW - Bayesian Belief Network

KW - Interestingness Measures

KW - Outlier Analysis

KW - Probabilistic Graphical Models

U2 - 10.1109/SECON.2015.7132918

DO - 10.1109/SECON.2015.7132918

M3 - Conference contribution

AN - SCOPUS:84938149645

SN - 9781467373005

T3 - SoutheastCon 2015

BT - IEEE SoutheastCon 2015 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE SoutheastCon 2015

Y2 - 9 April 2015 through 12 April 2015

ER -

Finding interesting outliers - A Belief Network based approach

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this