Article

Exception Rule Mining with a Relative Interestingness Measure

Authors:

Farhad Hussain,

Einoshin Suzuki,

Hongjun LuAuthors Info & Claims

PADKK '00: Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications

Pages 86 - 97

Published: 18 April 2000 Publication History

Abstract

This paper presents a method for mining exception rules based on a novel measure which estimates interestingness relative to its corresponding common sense rule and reference rule. Mining interesting rules is one of the important data mining tasks. Interesting rules bring novel knowledge that helps decision makers for advantageous actions. It is true that interestingness is a relative issue that depends on the other prior knowledge. However, this estimation can be biased due to the incomplete or inaccurate knowledge about the domain. Even if possible to estimate interestingness, it is not so trivial to judge the interestingness from a huge set of mined rules. Therefore, an automated system is required that can exploit the knowledge extractacted from the data in measuring interestingness. Since the extracted knowledge comes from the data, so it is possible to find a measure that is unbiased from the user's own belief. An unbiased measure that can estimate the interestingness of a rule with respect to the extractacted rules can be more acceptable to the user. In this work we try to show through the experiments, how our proposed relative measure can give an unbiased estimate of relative interestingness in a rule considering already mined rules.

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Cited By

Saikia SShroff GAgarwal PSrinivasan APandey AAnand GValluri SKarlapalem KSrivastava DBedathur S(2014)Exploratory data analysis using alternating covers of rules and exceptionsProceedings of the 20th International Conference on Management of Data10.5555/2726970.2726986(105-108)Online publication date: 17-Dec-2014
https://dl.acm.org/doi/10.5555/2726970.2726986
Carmona CGonzález PGarcía-Domingo Bdel Jesus MAguilera J(2013)MEFESKnowledge-Based Systems10.5555/2770961.277108254:C(73-85)Online publication date: 1-Dec-2013
https://dl.acm.org/doi/10.5555/2770961.2771082
Delgado MMartin-Bautista MRuiz MSánchez D(2013)Detecting Anomalous and Exceptional Behaviour on Credit Data by Means of Association RulesProceedings of the 10th International Conference on Flexible Query Answering Systems - Volume 813210.1007/978-3-642-40769-7_13(143-154)Online publication date: 18-Sep-2013
https://dl.acm.org/doi/10.1007/978-3-642-40769-7_13
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Exception Rule Mining with a Relative Interestingness Measure
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cover image Guide Proceedings

PADKK '00: Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications

April 2000

455 pages

ISBN:3540673822

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 18 April 2000

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Saikia SShroff GAgarwal PSrinivasan APandey AAnand GValluri SKarlapalem KSrivastava DBedathur S(2014)Exploratory data analysis using alternating covers of rules and exceptionsProceedings of the 20th International Conference on Management of Data10.5555/2726970.2726986(105-108)Online publication date: 17-Dec-2014
https://dl.acm.org/doi/10.5555/2726970.2726986
Carmona CGonzález PGarcía-Domingo Bdel Jesus MAguilera J(2013)MEFESKnowledge-Based Systems10.5555/2770961.277108254:C(73-85)Online publication date: 1-Dec-2013
https://dl.acm.org/doi/10.5555/2770961.2771082
Delgado MMartin-Bautista MRuiz MSánchez D(2013)Detecting Anomalous and Exceptional Behaviour on Credit Data by Means of Association RulesProceedings of the 10th International Conference on Flexible Query Answering Systems - Volume 813210.1007/978-3-642-40769-7_13(143-154)Online publication date: 18-Sep-2013
https://dl.acm.org/doi/10.1007/978-3-642-40769-7_13
Mansingh GOsei-Bryson KReichgelt H(2011)Using ontologies to facilitate post-processing of association rules by domain expertsInformation Sciences: an International Journal10.1016/j.ins.2010.09.027181:3(419-434)Online publication date: 1-Feb-2011
https://dl.acm.org/doi/10.1016/j.ins.2010.09.027
Terlecki P(2010)On the relation between jumping emerging patterns and rough set theory with application to data classificationTransactions on rough sets XII10.5555/1880429.1880442(236-338)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.5555/1880429.1880442
Zhou LYau SCandan KZhang Z(2007)Association rule and quantitative association rule mining among infrequent itemsProceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)10.1145/1341920.1341929(1-9)Online publication date: 12-Aug-2007
https://dl.acm.org/doi/10.1145/1341920.1341929
Plantevit MGoutier SGuisnel FLaurent ATeisseire MSong IPedersen T(2007)Mining unexpected multidimensional rulesProceedings of the ACM tenth international workshop on Data warehousing and OLAP10.1145/1317331.1317347(89-96)Online publication date: 9-Nov-2007
https://dl.acm.org/doi/10.1145/1317331.1317347
Terlecki PWalczak K(2007)Jumping emerging patterns with negation in transaction databases - Classification and discoveryInformation Sciences: an International Journal10.1016/j.ins.2007.07.018177:24(5675-5690)Online publication date: 20-Dec-2007
https://dl.acm.org/doi/10.1016/j.ins.2007.07.018
Liu BZhao KBenkler JXiao WEliassi-Rad TUngar LCraven MGunopulos D(2006)Rule interestingness analysis using OLAP operationsProceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/1150402.1150437(297-306)Online publication date: 20-Aug-2006
https://dl.acm.org/doi/10.1145/1150402.1150437
Morzy M(2006)Efficient mining of dissociation rulesProceedings of the 8th international conference on Data Warehousing and Knowledge Discovery10.1007/11823728_22(228-237)Online publication date: 4-Sep-2006
https://dl.acm.org/doi/10.1007/11823728_22
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