The application and effectiveness of a multi-objective metaheuristic algorithm for partial classification
In this paper, we present an application of multi-objective metaheuristics to the field of data mining. We introduce the data mining task of nugget discovery (also known as partial classification) and show how the multi-objective metaheuristic algorithm NSGA II can be modified to solve this problem....
Uloženo v:
| Vydáno v: | European journal of operational research Ročník 169; číslo 3; s. 898 - 917 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
16.03.2006
Elsevier Elsevier Sequoia S.A |
| Edice: | European Journal of Operational Research |
| Témata: | |
| ISSN: | 0377-2217, 1872-6860 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | In this paper, we present an application of multi-objective metaheuristics to the field of data mining. We introduce the data mining task of nugget discovery (also known as partial classification) and show how the multi-objective metaheuristic algorithm NSGA II can be modified to solve this problem. We also present an alternative algorithm for the same task, the ARAC algorithm, which can find all rules that are best according to some measures of interest subject to certain constraints. The ARAC algorithm provides an excellent basis for comparison with the results of the multi-objective metaheuristic algorithm as it can deliver the Pareto optimal front consisting of all partial classification rules that lie in the upper confidence/coverage border, for databases of limited size. We present the results of experiments with various well-known databases for both algorithms. We also discuss how the two methods can be used complementarily for large databases to deliver a set of best rules according to some predefined criteria, providing a powerful tool for knowledge discovery in databases. |
|---|---|
| AbstractList | In this paper, we present an application of multi-objective metaheuristics to the field of data mining. We introduce the data mining task of nugget discovery (also known as partial classification) and show how the multi-objective metaheuristic algorithm NSGA II can be modified to solve this problem. We also present an alternative algorithm for the same task, the ARAC algorithm, which can find all rules that are best according to some measures of interest subject to certain constraints. The ARAC algorithm provides an excellent basis for comparison with the results of the multi-objective metaheuristic algorithm as it can deliver the Pareto optimal front consisting of all partial classification rules that lie in the upper confidence/coverage border, for databases of limited size. We present the results of experiments with various well-known databases for both algorithms. We also discuss how the two methods can be used complementarily for large databases to deliver a set of best rules according to some predefined criteria, providing a powerful tool for knowledge discovery in databases. In this paper, we present an application of multi-objective metaheuristics to the field of data mining. We introduce the data mining task of nugget discovery (also known as partial classification) and show how the multi-objective metaheuristic algorithm NSGA II can be modified to solve this problem. We also present an alternative algorithm for the same task, the ARAC algorithm, which can find all rules that are best according to some measures of interest subject to certain constraints. The ARAC algorithm provides an excellent basis for comparison with the results of the multi-objective metaheuristic algorithm as it can deliver the Pareto optimal front consisting of all partial classification rules that lie in the upper confidence/coverage border, for databases of limited size. We present the results of experiments with various well-known databases for both algorithms. We also discuss how the two methods can be used complementarily for large databases to deliver a set of best rules according to some predefined criteria, providing a powerful tool for knowledge discovery in databases. [PUBLICATION ABSTRACT] |
| Author | Richards, G. Rayward-Smith, V.J. Philpott, M.S. de la Iglesia, B. |
| Author_xml | – sequence: 1 givenname: B. surname: de la Iglesia fullname: de la Iglesia, B. email: bli@cmp.uea.ac.uk – sequence: 2 givenname: G. surname: Richards fullname: Richards, G. email: gr@cmp.uea.ac.uk – sequence: 3 givenname: M.S. surname: Philpott fullname: Philpott, M.S. email: m.s.philpott@cmp.uea.ac.uk – sequence: 4 givenname: V.J. surname: Rayward-Smith fullname: Rayward-Smith, V.J. email: vjrs@cmp.uea.ac.uk |
| BackLink | http://econpapers.repec.org/article/eeeejores/v_3a169_3ay_3a2006_3ai_3a3_3ap_3a898-917.htm$$DView record in RePEc |
| BookMark | eNp9kU9v1DAQxS1UJLaFL8DJ4p5l7MR_InFBFRREJS7lbHmdMesoiYPtXanfHm9TceBQS89z8LyfR2-uydUSFyTkPYM9AyY_jnscY9pzgG4Peg9cvCI7phVvpJZwRXbQKtVwztQbcp3zCABMMLEj4eGI1K7rFJwtIS7ULgNF79GVcMYFc6bRU0vn01RCEw_j9kBnLPaIpxRyCY7a6XdMoRxn6mOiq00l2Im6yeYc_DP5LXnt7ZTx3XO9Ib--fnm4_dbc_7z7fvv5vnGdZKU5gHS-jid5h04wr6RAByilb1k3YC9sP2gvNBeA1h3cMHCLTnaDEMprpdob8mHjrin-OWEuZoyntNQvDYeOtarjojb92JoSrujMmsJs06PBemqOmM3ZtJbJvt6PVTVXWUuoaqvWKt1r0zNljmWuNL7RXIo5J_T_gAzMZT9mNBeuuezHgDbwNIL-z-RCeUqqJBuml62fNivWHM8Bk8ku4OJwCKnuxwwxvGT_C7i2r-o |
| CODEN | EJORDT |
| CitedBy_id | crossref_primary_10_1016_j_eswa_2019_113163 crossref_primary_10_4018_ijssci_2014040101 crossref_primary_10_1002_widm_1106 crossref_primary_10_1007_s00500_008_0320_1 crossref_primary_10_1007_s10288_019_00402_4 crossref_primary_10_1007_s10796_016_9690_6 crossref_primary_10_1007_s11831_024_10157_9 crossref_primary_10_4018_jamc_2011040103 crossref_primary_10_1109_TEVC_2013_2281396 crossref_primary_10_1007_s10844_012_0232_5 crossref_primary_10_1007_s10479_021_04496_0 crossref_primary_10_1016_j_ejor_2012_03_039 crossref_primary_10_1080_13032917_2007_9687209 crossref_primary_10_1016_j_tre_2019_11_010 crossref_primary_10_1587_transinf_2014EDP7069 |
| Cites_doi | 10.1080/02664769100000005 10.1007/BF00962821 10.1023/A:1009895914772 10.1007/BF00116835 10.1016/S1042-8143(05)80025-1 10.1023/A:1022631118932 10.1145/312129.312217 10.1162/evco.1994.2.3.221 10.1016/S0950-7051(99)00019-2 10.1080/08839519408945435 |
| ContentType | Journal Article |
| Copyright | 2005 Elsevier B.V. Copyright Elsevier Sequoia S.A. Mar 16, 2006 |
| Copyright_xml | – notice: 2005 Elsevier B.V. – notice: Copyright Elsevier Sequoia S.A. Mar 16, 2006 |
| DBID | AAYXX CITATION DKI X2L 7SC 7TB 8FD FR3 JQ2 L7M L~C L~D |
| DOI | 10.1016/j.ejor.2004.08.025 |
| DatabaseName | CrossRef RePEc IDEAS RePEc Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science Business |
| EISSN | 1872-6860 |
| EndPage | 917 |
| ExternalDocumentID | 904830101 eeeejores_v_3a169_3ay_3a2006_3ai_3a3_3ap_3a898_917_htm 10_1016_j_ejor_2004_08_025 S0377221704005727 |
| Genre | Feature |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1OL 1RT 1~. 1~5 29G 4.4 41~ 457 4G. 5GY 5VS 6OB 7-5 71M 8P~ 9JN 9JO AAAKF AAAKG AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXUO AAYFN AAYOK ABAOU ABBOA ABFNM ABFRF ABJNI ABMAC ABUCO ABXDB ABYKQ ACAZW ACDAQ ACGFO ACGFS ACIWK ACNCT ACNNM ACRLP ACZNC ADBBV ADEZE ADGUI ADIYS ADJOM ADMUD AEBSH AEFWE AEKER AENEX AFFNX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHZHX AI. AIALX AIEXJ AIGVJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR ASPBG AVWKF AXJTR AZFZN BKOJK BKOMP BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX HVGLF HZ~ IHE J1W KOM LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 R2- RIG ROL RPZ RXW SCC SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SSV SSW SSZ T5K TAE TN5 U5U VH1 WUQ XPP ZMT ~02 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADXHL AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 02 08R 0R 1 41 6XO 8P AAPBV ABFLS ADALY DKI G- HZ IPNFZ K M MS PQEST STF X X2L 7SC 7TB 8FD AFXIZ AGCQF AGRNS FR3 JQ2 L7M L~C L~D SSH |
| ID | FETCH-LOGICAL-c461t-b06cf000624ec51f765ec0e66f314de95a9d8f58250eacbcdd2aec64d557f8773 |
| ISICitedReferencesCount | 31 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000232681400015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0377-2217 |
| IngestDate | Fri Jul 25 04:30:45 EDT 2025 Wed Aug 18 03:50:57 EDT 2021 Sat Nov 29 01:40:42 EST 2025 Tue Nov 18 21:07:20 EST 2025 Fri Feb 23 02:32:19 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | Multi-objective metaheuristics Data mining Association rule discovery Rule extraction |
| Language | English |
| License | https://www.elsevier.com/tdm/userlicense/1.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c461t-b06cf000624ec51f765ec0e66f314de95a9d8f58250eacbcdd2aec64d557f8773 |
| Notes | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 |
| PQID | 204137425 |
| PQPubID | 45678 |
| PageCount | 20 |
| ParticipantIDs | proquest_journals_204137425 repec_primary_eeeejores_v_3a169_3ay_3a2006_3ai_3a3_3ap_3a898_917_htm crossref_primary_10_1016_j_ejor_2004_08_025 crossref_citationtrail_10_1016_j_ejor_2004_08_025 elsevier_sciencedirect_doi_10_1016_j_ejor_2004_08_025 |
| PublicationCentury | 2000 |
| PublicationDate | 2006-03-16 |
| PublicationDateYYYYMMDD | 2006-03-16 |
| PublicationDate_xml | – month: 03 year: 2006 text: 2006-03-16 day: 16 |
| PublicationDecade | 2000 |
| PublicationPlace | Amsterdam |
| PublicationPlace_xml | – name: Amsterdam |
| PublicationSeriesTitle | European Journal of Operational Research |
| PublicationTitle | European journal of operational research |
| PublicationYear | 2006 |
| Publisher | Elsevier B.V Elsevier Elsevier Sequoia S.A |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier – name: Elsevier Sequoia S.A |
| References | Deb, Agrawal, Pratap, Meyarivan (bib34) 2000; vol. 1917 Holte (bib18) 1993; 11 Breiman, Friedman, Olshen, Stone (bib4) 1984 Debuse, de la Iglesia, Howard, Rayward-Smith (bib2) 2000 Bayardo, Agrawal (bib13) 2000; 4 Agrawal, Imielinski, Swami (bib10) 1993 de la Iglesia, Debuse, Rayward-Smith (bib30) 1996 Srikant, Agrawal (bib15) 1996 Biggs, de Ville, Suen (bib24) 1991; 18 Freitas (bib26) 1999; 12 Agrawal, Srikant (bib11) 1994 Y. Morimoto, T. Fukuda, H. Matsuzawa, T. Tokuyama, K. Yoda, Algorithms for mining association rules for binary segmentations of huge categorical databases, in: Proceedings of the 24th Very Large Data Bases conference, 1998, pp. 380–391. Richards, Rayward-Smith (bib36) 2005; 9 (bib1) 1996 Clark, Niblett (bib5) 1989; 3 Cohen (bib6) 1995 B. de la Iglesia, The development and application of heuristic techniques for the data mining task of nugget discovery, Ph.D. thesis, University of East Anglia, 2001. Han, Pei, Yin (bib12) 2000 Srinivas, Deb (bib35) 1995; 2 de la Iglesia, Rayward-Smith (bib31) 2002 Quinlan (bib3) 1993 Gebhardt (bib27) 1991; 3 Freitas (bib25) 1998; vol. 1510 E. Zitzler, L. Thiele, An evolutionary algorithm for multi-objective optimization: The strength Pareto approach, Technical Report No. 43. Computer Engineering and Networks Laboratory, Switzerland, 1998. Ali, Manganaris, Srikant (bib7) 1997 Bayardo, Agrawal (bib14) 1999 S. Brin, R. Rastogi, K. Shim, Mining optimized gain rules for numeric attributes, in: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999, pp. 135–144. Zighed, Rakotomalala, Feschet (bib16) 1997 Fukuda, Morimoto, Morishita, Tokuyam (bib17) 1996 . Horn, Nafpliotis, Goldberg (bib32) 1994; vol. 1 Riddle, Segal, Etzioni (bib8) 1994; 8 P.C. Clark, R. Boswell, Rule induction with CN2: Some recent improvements, in: Y. Kodratoff (Ed.), Machine Learning—Proceedings of the Fifth European Conference, Springer-Verlag, Berlin. International Business Machines, IBM Intelligent Miner. User’s Guide, Version 1, Release 1, 1997. Richards, Rayward-Smith (bib37) 2001 C.J. Merz, P.M. Murphy, UCI repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences, 1998. Available from Knowles, Corne (bib33) 1999; vol. 1 Major, Mangano (bib28) 1995; 4 Piatetsky-Shapiro (bib22) 1991 Cohen (10.1016/j.ejor.2004.08.025_bib6) 1995 Freitas (10.1016/j.ejor.2004.08.025_bib25) 1998; vol. 1510 Gebhardt (10.1016/j.ejor.2004.08.025_bib27) 1991; 3 Agrawal (10.1016/j.ejor.2004.08.025_bib11) 1994 10.1016/j.ejor.2004.08.025_bib9 10.1016/j.ejor.2004.08.025_bib29 Clark (10.1016/j.ejor.2004.08.025_bib5) 1989; 3 Zighed (10.1016/j.ejor.2004.08.025_bib16) 1997 Holte (10.1016/j.ejor.2004.08.025_bib18) 1993; 11 10.1016/j.ejor.2004.08.025_bib23 10.1016/j.ejor.2004.08.025_bib21 10.1016/j.ejor.2004.08.025_bib20 Fukuda (10.1016/j.ejor.2004.08.025_bib17) 1996 Biggs (10.1016/j.ejor.2004.08.025_bib24) 1991; 18 Bayardo (10.1016/j.ejor.2004.08.025_bib13) 2000; 4 Piatetsky-Shapiro (10.1016/j.ejor.2004.08.025_bib22) 1991 (10.1016/j.ejor.2004.08.025_bib1) 1996 Breiman (10.1016/j.ejor.2004.08.025_bib4) 1984 de la Iglesia (10.1016/j.ejor.2004.08.025_bib30) 1996 Ali (10.1016/j.ejor.2004.08.025_bib7) 1997 Srinivas (10.1016/j.ejor.2004.08.025_bib35) 1995; 2 Riddle (10.1016/j.ejor.2004.08.025_bib8) 1994; 8 10.1016/j.ejor.2004.08.025_bib19 Bayardo (10.1016/j.ejor.2004.08.025_bib14) 1999 10.1016/j.ejor.2004.08.025_bib38 Richards (10.1016/j.ejor.2004.08.025_bib37) 2001 Agrawal (10.1016/j.ejor.2004.08.025_bib10) 1993 Horn (10.1016/j.ejor.2004.08.025_bib32) 1994; vol. 1 Han (10.1016/j.ejor.2004.08.025_bib12) 2000 Deb (10.1016/j.ejor.2004.08.025_bib34) 2000; vol. 1917 Freitas (10.1016/j.ejor.2004.08.025_bib26) 1999; 12 Debuse (10.1016/j.ejor.2004.08.025_bib2) 2000 Srikant (10.1016/j.ejor.2004.08.025_bib15) 1996 Richards (10.1016/j.ejor.2004.08.025_bib36) 2005; 9 Major (10.1016/j.ejor.2004.08.025_bib28) 1995; 4 Quinlan (10.1016/j.ejor.2004.08.025_bib3) 1993 Knowles (10.1016/j.ejor.2004.08.025_bib33) 1999; vol. 1 de la Iglesia (10.1016/j.ejor.2004.08.025_bib31) 2002 |
| References_xml | – volume: 8 start-page: 125 year: 1994 end-page: 147 ident: bib8 article-title: Representation design and brute-force induction in a Boeing manufacturing domain publication-title: Applied Artificial Intelligence – start-page: 72 year: 2002 end-page: 96 ident: bib31 article-title: The discovery of interesting nuggets using heuristic techniques publication-title: Data Mining: A Heuristic Approach – start-page: 1 year: 2000 end-page: 12 ident: bib12 article-title: Mining frequent patterns without candidate generation publication-title: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (SIGMOD ’00) – reference: S. Brin, R. Rastogi, K. Shim, Mining optimized gain rules for numeric attributes, in: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999, pp. 135–144. – year: 1993 ident: bib3 article-title: C4.5: Programs for Machine Learning – start-page: 1 year: 1996 end-page: 12 ident: bib15 article-title: Mining quantitative association rules in large relational tables publication-title: Proceedings of the ACM-SIGMOD 1996 Conference on Management of Data, June 1996 – reference: E. Zitzler, L. Thiele, An evolutionary algorithm for multi-objective optimization: The strength Pareto approach, Technical Report No. 43. Computer Engineering and Networks Laboratory, Switzerland, 1998. – reference: Y. Morimoto, T. Fukuda, H. Matsuzawa, T. Tokuyama, K. Yoda, Algorithms for mining association rules for binary segmentations of huge categorical databases, in: Proceedings of the 24th Very Large Data Bases conference, 1998, pp. 380–391. – reference: P.C. Clark, R. Boswell, Rule induction with CN2: Some recent improvements, in: Y. Kodratoff (Ed.), Machine Learning—Proceedings of the Fifth European Conference, Springer-Verlag, Berlin. – volume: 9 year: 2005 ident: bib36 article-title: The discovery of association rules from tabular databases comprising nominal and ordinal attributes publication-title: Journal of Intelligent Data Analysis – volume: vol. 1 start-page: 82 year: 1994 end-page: 87 ident: bib32 article-title: A niched Pareto genetic algorithm for multiobjective optimization publication-title: Proceedings of the First IEEE Conference on Evolutionary Computation – start-page: 115 year: 1995 end-page: 123 ident: bib6 article-title: Fast effective rule induction publication-title: Proceedings of Twelfth International Conference on Machine Learning (ICML-95) – volume: vol. 1917 start-page: 849 year: 2000 end-page: 858 ident: bib34 article-title: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II publication-title: Proceedings of the Parallel Problem Solving from Nature VI Conference – start-page: 487 year: 1994 end-page: 499 ident: bib11 article-title: Fast algorithms for mining association rules publication-title: Proceedings of the 20th International Conference on VLDB, September 1994 – start-page: 182 year: 1996 end-page: 191 ident: bib17 article-title: Mining optimized association rules for numeric attributes publication-title: Proceedings of the 15th ACM SIGACT–SIGMOD–SIGART Symposium on Principles of Database Systems (PODS’96), June 1996 – volume: vol. 1 start-page: 98 year: 1999 end-page: 105 ident: bib33 article-title: The Pareto archived evolution strategy: A new baseline algorithm for Pareto multi-objective optimisation publication-title: Proceedings of the Congress on Evolutionary Computation, Mayflower Hotel, Washington DC, USA, 6–9 – year: 2000 ident: bib2 article-title: Building the KDD roadmap: A methodology for knowledge discovery publication-title: Industrial Knowledge Management – volume: 4 start-page: 39 year: 1995 end-page: 52 ident: bib28 article-title: Selecting among rules induced from a hurricane database publication-title: Journal of Intelligent Information Systems – start-page: 465 year: 2001 end-page: 472 ident: bib37 article-title: Discovery of association rules in tabular data publication-title: IEEE International Conference on Data Mining, November 2001 – year: 1996 ident: bib1 publication-title: Advances in Knowledge Discovery and Data Mining – start-page: 115 year: 1997 end-page: 118 ident: bib7 article-title: Partial classification using association rules publication-title: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, (KDD 99), August 1999 – volume: 12 start-page: 309 year: 1999 end-page: 315 ident: bib26 article-title: On rule interestingness measures publication-title: Knowledge-Based Systems Journal – start-page: 44 year: 1996 end-page: 49 ident: bib30 article-title: Discovering knowledge in commercial databases using modern heuristic techniques publication-title: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining – volume: 18 start-page: 49 year: 1991 end-page: 62 ident: bib24 article-title: A method of choosing multiway partitions for classification and decision trees publication-title: Journal of Applied Statistics – year: 1984 ident: bib4 article-title: Classification and Regression Trees – reference: C.J. Merz, P.M. Murphy, UCI repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences, 1998. Available from: – start-page: 295 year: 1997 end-page: 298 ident: bib16 article-title: Optimal multiple intervals discretization of continuous attributes for supervised learning publication-title: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD 97) – volume: 11 start-page: 69 year: 1993 end-page: 91 ident: bib18 article-title: Very simple classification rules perform well on most commonly used datasets publication-title: Machine Learning – volume: 2 start-page: 221 year: 1995 end-page: 248 ident: bib35 article-title: Multi-objective function optimization using non-dominated sorting genetic algorithm publication-title: Evolutionary Computation – reference: B. de la Iglesia, The development and application of heuristic techniques for the data mining task of nugget discovery, Ph.D. thesis, University of East Anglia, 2001. – volume: 3 start-page: 361 year: 1991 end-page: 380 ident: bib27 article-title: Choosing among competing generalisations publication-title: Knowledge Acquisition – volume: vol. 1510 start-page: 1 year: 1998 end-page: 9 ident: bib25 article-title: On objective measures of rule surprisingness publication-title: Principles of Data Mining and Knowledge Discovery (Proceedings of the 2nd European Symposium, PKDD’98, Nantes, France) – start-page: 145 year: 1999 end-page: 153 ident: bib14 article-title: Mining the most interesting rules publication-title: Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining (KDD 99), August 1999 – volume: 3 start-page: 261 year: 1989 end-page: 284 ident: bib5 article-title: The CN2 induction algorithm publication-title: Machine Learning – volume: 4 start-page: 217 year: 2000 end-page: 240 ident: bib13 article-title: Constraint based rule mining in large dense databases publication-title: Data Mining and Knowledge Discovery Journal – reference: International Business Machines, IBM Intelligent Miner. User’s Guide, Version 1, Release 1, 1997. – start-page: 229 year: 1991 end-page: 248 ident: bib22 article-title: Discovery, analysis and presentation of strong rules publication-title: Knowledge Discovery in Databases – start-page: 207 year: 1993 end-page: 216 ident: bib10 article-title: Mining association rules between sets of items in large databases publication-title: Proceedings of the ACM SIGMOD International Conference on Management of Data, May 1993 – reference: . – start-page: 1 year: 2000 ident: 10.1016/j.ejor.2004.08.025_bib12 article-title: Mining frequent patterns without candidate generation – start-page: 44 year: 1996 ident: 10.1016/j.ejor.2004.08.025_bib30 article-title: Discovering knowledge in commercial databases using modern heuristic techniques – volume: 18 start-page: 49 issue: 1 year: 1991 ident: 10.1016/j.ejor.2004.08.025_bib24 article-title: A method of choosing multiway partitions for classification and decision trees publication-title: Journal of Applied Statistics doi: 10.1080/02664769100000005 – volume: 4 start-page: 39 issue: 1 year: 1995 ident: 10.1016/j.ejor.2004.08.025_bib28 article-title: Selecting among rules induced from a hurricane database publication-title: Journal of Intelligent Information Systems doi: 10.1007/BF00962821 – volume: 4 start-page: 217 year: 2000 ident: 10.1016/j.ejor.2004.08.025_bib13 article-title: Constraint based rule mining in large dense databases publication-title: Data Mining and Knowledge Discovery Journal doi: 10.1023/A:1009895914772 – volume: vol. 1917 start-page: 849 year: 2000 ident: 10.1016/j.ejor.2004.08.025_bib34 article-title: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II – start-page: 295 year: 1997 ident: 10.1016/j.ejor.2004.08.025_bib16 article-title: Optimal multiple intervals discretization of continuous attributes for supervised learning – volume: vol. 1510 start-page: 1 year: 1998 ident: 10.1016/j.ejor.2004.08.025_bib25 article-title: On objective measures of rule surprisingness – volume: 3 start-page: 261 year: 1989 ident: 10.1016/j.ejor.2004.08.025_bib5 article-title: The CN2 induction algorithm publication-title: Machine Learning doi: 10.1007/BF00116835 – start-page: 487 year: 1994 ident: 10.1016/j.ejor.2004.08.025_bib11 article-title: Fast algorithms for mining association rules – year: 1996 ident: 10.1016/j.ejor.2004.08.025_bib1 – volume: 3 start-page: 361 year: 1991 ident: 10.1016/j.ejor.2004.08.025_bib27 article-title: Choosing among competing generalisations publication-title: Knowledge Acquisition doi: 10.1016/S1042-8143(05)80025-1 – start-page: 1 year: 1996 ident: 10.1016/j.ejor.2004.08.025_bib15 article-title: Mining quantitative association rules in large relational tables – volume: 11 start-page: 69 year: 1993 ident: 10.1016/j.ejor.2004.08.025_bib18 article-title: Very simple classification rules perform well on most commonly used datasets publication-title: Machine Learning doi: 10.1023/A:1022631118932 – ident: 10.1016/j.ejor.2004.08.025_bib20 doi: 10.1145/312129.312217 – start-page: 115 year: 1995 ident: 10.1016/j.ejor.2004.08.025_bib6 article-title: Fast effective rule induction – volume: 2 start-page: 221 issue: 3 year: 1995 ident: 10.1016/j.ejor.2004.08.025_bib35 article-title: Multi-objective function optimization using non-dominated sorting genetic algorithm publication-title: Evolutionary Computation doi: 10.1162/evco.1994.2.3.221 – start-page: 182 year: 1996 ident: 10.1016/j.ejor.2004.08.025_bib17 article-title: Mining optimized association rules for numeric attributes – start-page: 229 year: 1991 ident: 10.1016/j.ejor.2004.08.025_bib22 article-title: Discovery, analysis and presentation of strong rules – start-page: 207 year: 1993 ident: 10.1016/j.ejor.2004.08.025_bib10 article-title: Mining association rules between sets of items in large databases – ident: 10.1016/j.ejor.2004.08.025_bib21 – ident: 10.1016/j.ejor.2004.08.025_bib23 – year: 1993 ident: 10.1016/j.ejor.2004.08.025_bib3 – volume: 12 start-page: 309 issue: 5–6 year: 1999 ident: 10.1016/j.ejor.2004.08.025_bib26 article-title: On rule interestingness measures publication-title: Knowledge-Based Systems Journal doi: 10.1016/S0950-7051(99)00019-2 – ident: 10.1016/j.ejor.2004.08.025_bib29 – volume: 9 issue: 3 year: 2005 ident: 10.1016/j.ejor.2004.08.025_bib36 article-title: The discovery of association rules from tabular databases comprising nominal and ordinal attributes publication-title: Journal of Intelligent Data Analysis – start-page: 72 year: 2002 ident: 10.1016/j.ejor.2004.08.025_bib31 article-title: The discovery of interesting nuggets using heuristic techniques – volume: vol. 1 start-page: 98 year: 1999 ident: 10.1016/j.ejor.2004.08.025_bib33 article-title: The Pareto archived evolution strategy: A new baseline algorithm for Pareto multi-objective optimisation – year: 1984 ident: 10.1016/j.ejor.2004.08.025_bib4 – ident: 10.1016/j.ejor.2004.08.025_bib9 – ident: 10.1016/j.ejor.2004.08.025_bib38 – year: 2000 ident: 10.1016/j.ejor.2004.08.025_bib2 article-title: Building the KDD roadmap: A methodology for knowledge discovery – volume: vol. 1 start-page: 82 year: 1994 ident: 10.1016/j.ejor.2004.08.025_bib32 article-title: A niched Pareto genetic algorithm for multiobjective optimization – start-page: 465 year: 2001 ident: 10.1016/j.ejor.2004.08.025_bib37 article-title: Discovery of association rules in tabular data – volume: 8 start-page: 125 year: 1994 ident: 10.1016/j.ejor.2004.08.025_bib8 article-title: Representation design and brute-force induction in a Boeing manufacturing domain publication-title: Applied Artificial Intelligence doi: 10.1080/08839519408945435 – ident: 10.1016/j.ejor.2004.08.025_bib19 – start-page: 145 year: 1999 ident: 10.1016/j.ejor.2004.08.025_bib14 article-title: Mining the most interesting rules – start-page: 115 year: 1997 ident: 10.1016/j.ejor.2004.08.025_bib7 article-title: Partial classification using association rules |
| SSID | ssj0001515 |
| Score | 1.9780784 |
| Snippet | In this paper, we present an application of multi-objective metaheuristics to the field of data mining. We introduce the data mining task of nugget discovery... |
| SourceID | proquest repec crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 898 |
| SubjectTerms | Algorithms Association rule discovery Data mining Effectiveness Heuristic Multi-objective metaheuristics Operations research Pareto optimum Rule extraction Studies |
| Title | The application and effectiveness of a multi-objective metaheuristic algorithm for partial classification |
| URI | https://dx.doi.org/10.1016/j.ejor.2004.08.025 http://econpapers.repec.org/article/eeeejores/v_3a169_3ay_3a2006_3ai_3a3_3ap_3a898-917.htm https://www.proquest.com/docview/204137425 |
| Volume | 169 |
| WOSCitedRecordID | wos000232681400015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6860 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001515 issn: 0377-2217 databaseCode: AIEXJ dateStart: 19950105 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLbKihA8cClDjAHyA-KlStUkTpw8VtCJoakg1qG-WYnjrKm6JutN25_lt3Ac22kyoIIHKjmt7ObSnmP78_E550PoHfU5wFRYltgi6FvE4b4VJn1uUS8WtB-FLiFxSTZBR6NgMgm_tlo_TCzMdk4Xi-DmJiz-q6ihDoQtQ2f_QdzVRaECPoPQ4Qhih-NfC762LV3uDiivDTOwlSGRpSehlccz1SCppKOp2KjEzd1ofpkvs_X0qnRDLORtZCIRCbWlb9FOnL8z6muACxVLY2rUOYUq23MiuvOoe3o5F6usSf6sI_0Nlftuz6IwriU7a-236LZ0-q3MQ9_1LlfNjOFaKspS2dZMfM25uN7kWdQ97w1qQ6FLqeU4KsqzJ9RQHVDH8gPFRlCN5Yr3RSutWxuZA0V2_cuMoYwXs56Y5WV6WFJmdFXB2M303KMv7OTi7IyNh5Nxs7WEA6FMzi8z9r0vri3JaiZ3_zXFyz3UdqgXwsTRHpwOJ58rrCDhZLnPpX-fDutSHoh3H-lP0Km2NGovRSF4DSGNn6LHemmDB0oln6GWWHTQAxNZ0UFPDIMI1hNKBz2qpcN8jjJQXVxTXQyqixuqi_MUR_iO6uKG6uJKdTGoLtaqi5uqe4guTobjD58sTQViceLbayvu-zyV0Mohgnt2Sn1P8L7w_dS1SSJCLwqTIPUCAPSAJGKeJE4kuE8Sz6NpQKn7Ah0s8oV4iXBC4jimMHWlxCdyOe5TN4FVCIn7EY2S9AjZ5l9mXOfJl3Qtc2YcImdMSkYSuBImOVwd7wh1q3MKlSVm77c9Izymca7CrwyUcu95x0bSTPfmFbQDDKVEtn4shV89gIAXXEGs2Ja5EfQMON5Ckb0P3jIoLpQCCnQOFtqUTddXr_be5Bg93HXe1-hgvdyIN-g-366z1fKt1u6fhfrrww |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=The+application+and+effectiveness+of+a+multi-objective+metaheuristic+algorithm+for+partial+classification&rft.jtitle=European+journal+of+operational+research&rft.au=de+la+Iglesia%2C+B&rft.au=Richards%2C+G&rft.au=Philpott%2C+M+S&rft.au=Rayward-Smith%2C+V+J&rft.date=2006-03-16&rft.pub=Elsevier+Sequoia+S.A&rft.issn=0377-2217&rft.eissn=1872-6860&rft.volume=169&rft.issue=3&rft.spage=898&rft_id=info:doi/10.1016%2Fj.ejor.2004.08.025&rft.externalDBID=NO_FULL_TEXT&rft.externalDocID=904830101 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0377-2217&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0377-2217&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0377-2217&client=summon |