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article

Sequential pattern mining algorithm for automotive warranty data

Authors:

Jirachai Buddhakulsomsiri,

Armen ZakarianAuthors Info & Claims

Computers and Industrial Engineering, Volume 57, Issue 1

Pages 137 - 147

https://doi.org/10.1016/j.cie.2008.11.006

Published: 01 August 2009 Publication History

Abstract

This paper presents a sequential pattern mining algorithm that allows product and quality engineers to extract hidden knowledge from a large automotive warranty database. The algorithm uses the elementary set concept and database manipulation techniques to search for patterns or relationships among occurrences of warranty claims over time. These patterns are represented as IF-THEN sequential rules, where the IF portion of the rule includes one or more occurrences of warranty problems at one time and the THEN portion includes warranty problem(s) that occur at a later time. Once sequential patterns are generated, the algorithm uses rule strength parameters to filter out insignificant patterns, so that only important (significant) rules are reported. Significant patterns provide knowledge of one or more product failures that leads to future product fault(s). The effectiveness of the algorithm is illustrated with the warranty data mining application from the automotive industry. A discussion on the sequential patterns generated by the algorithm and their interpretation for the automotive example are also provided.

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Cited By

Yadav P(2022)Hybridized optimization oriented fast negative sequential patterns miningMultimedia Tools and Applications10.1007/s11042-021-11773-481:4(5279-5303)Online publication date: 1-Feb-2022
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Rajpathak DChougule RBandyopadhyay P(2018)A domain-specific decision support system for knowledge discovery using association and text miningKnowledge and Information Systems10.5555/3225631.322574831:3(405-432)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.5555/3225631.3225748
Prytz RNowaczyk SRögnvaldsson TByttner S(2018)Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle dataEngineering Applications of Artificial Intelligence10.1016/j.engappai.2015.02.00941:C(139-150)Online publication date: 27-Dec-2018
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Sequential pattern mining algorithm for automotive warranty data
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Published In

cover image Computers and Industrial Engineering

Computers and Industrial Engineering Volume 57, Issue 1

August, 2009

445 pages

ISSN:0360-8352

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2008.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 August 2009

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Cited By

Yadav P(2022)Hybridized optimization oriented fast negative sequential patterns miningMultimedia Tools and Applications10.1007/s11042-021-11773-481:4(5279-5303)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11042-021-11773-4
Rajpathak DChougule RBandyopadhyay P(2018)A domain-specific decision support system for knowledge discovery using association and text miningKnowledge and Information Systems10.5555/3225631.322574831:3(405-432)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.5555/3225631.3225748
Prytz RNowaczyk SRögnvaldsson TByttner S(2018)Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle dataEngineering Applications of Artificial Intelligence10.1016/j.engappai.2015.02.00941:C(139-150)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1016/j.engappai.2015.02.009
Bergmeir PNitsche CNonnast JBargende M(2016)Classifying component failures of a hybrid electric vehicle fleet based on load spectrum dataNeural Computing and Applications10.1007/s00521-015-2065-y27:8(2289-2304)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1007/s00521-015-2065-y
Kamsu-Foguem BRigal FMauget F(2013)Mining association rules for the quality improvement of the production processExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.08.03940:4(1034-1045)Online publication date: 1-Mar-2013
https://dl.acm.org/doi/10.1016/j.eswa.2012.08.039
Martínez-de-Pisón FSanz AMartínez-de-Pisón EJiménez EConti D(2012)Mining association rules from time series to explain failures in a hot-dip galvanizing steel lineComputers and Industrial Engineering10.1016/j.cie.2012.01.01363:1(22-36)Online publication date: 1-Aug-2012
https://dl.acm.org/doi/10.1016/j.cie.2012.01.013

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