Mining associative classification rules with stock trading data – A GA-based method
Associative classifiers are a classification system based on associative classification rules. Although associative classification is more accurate than a traditional classification approach, it cannot handle numerical data and its relationships. Therefore, an ongoing research problem is how to buil...
Saved in:
| Published in | Knowledge-based systems Vol. 23; no. 6; pp. 605 - 614 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.08.2010
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0950-7051 1872-7409 |
| DOI | 10.1016/j.knosys.2010.04.007 |
Cover
| Abstract | Associative classifiers are a classification system based on associative classification rules. Although associative classification is more accurate than a traditional classification approach, it cannot handle numerical data and its relationships. Therefore, an ongoing research problem is how to build associative classifiers from numerical data. In this work, we focus on stock trading data with many numerical technical indicators, and the classification problem is finding sell and buy signals from the technical indicators. This study proposes a GA-based algorithm used to build an associative classifier that can discover trading rules from these numerical indicators. The experiment results show that the proposed approach is an effective classification technique with high prediction accuracy and is highly competitive when compared with the data distribution method. |
|---|---|
| AbstractList | Associative classifiers are a classification system based on associative classification rules. Although associative classification is more accurate than a traditional classification approach, it cannot handle numerical data and its relationships. Therefore, an ongoing research problem is how to build associative classifiers from numerical data. In this work, we focus on stock trading data with many numerical technical indicators, and the classification problem is finding sell and buy signals from the technical indicators. This study proposes a GA-based algorithm used to build an associative classifier that can discover trading rules from these numerical indicators. The experiment results show that the proposed approach is an effective classification technique with high prediction accuracy and is highly competitive when compared with the data distribution method. |
| Author | Chang Chien, Ya-Wen Chen, Yen-Liang |
| Author_xml | – sequence: 1 givenname: Ya-Wen surname: Chang Chien fullname: Chang Chien, Ya-Wen email: ywcc@mgt.ncu.edu.tw – sequence: 2 givenname: Yen-Liang surname: Chen fullname: Chen, Yen-Liang email: ylchen@mgt.ncu.edu.tw |
| BookMark | eNqFkMFOAjEQhhujiYC-gYe-wOLsbrtlPZgQomiC8SLnprRTKSxb01YMN9_BN_RJXMSTBz1NZpLvz_xfnxy3vkVCLnIY5pBXl6vhuvVxF4cFdCdgQwBxRHr5SBSZYFAfkx7UHDIBPD8l_RhXAFAU-ahH5g-ude0zVTF67VRyW6S66TZnne5W39Lw2mCkby4taUxer2kKyuwZo5Kin-8fdEyn42yhIhq6wbT05oycWNVEPP-ZAzK_vXma3GWzx-n9ZDzLdAlVyspqIbQQxhiNDEuGwijDUHW_GstFgQatqZFrwUc1LHhlNWO2MLbkZcFzXg4IO-Tq4GMMaOVLcBsVdjIHuVcjV_KgRu7VSGCyU9NhV78w7dJ32a6aa_6Drw8wdsW2DoOM2mGr0biAOknj3d8BX3DahyQ |
| CitedBy_id | crossref_primary_10_1016_j_asoc_2018_03_051 crossref_primary_10_1007_s11432_013_4985_4 crossref_primary_10_1186_s13673_020_00240_y crossref_primary_10_1109_TCYB_2014_2370063 crossref_primary_10_1016_j_knosys_2011_01_012 crossref_primary_10_1155_2014_861641 crossref_primary_10_1016_j_eswa_2022_116523 crossref_primary_10_1080_08839514_2019_1630124 crossref_primary_10_1007_s00500_017_2800_7 crossref_primary_10_1016_j_knosys_2014_08_010 crossref_primary_10_1016_j_knosys_2011_08_012 crossref_primary_10_1016_j_knosys_2014_04_018 crossref_primary_10_1142_S0129626414500017 crossref_primary_10_1016_j_knosys_2011_04_013 crossref_primary_10_1007_s00607_019_00773_w crossref_primary_10_1016_j_knosys_2013_06_007 crossref_primary_10_1142_S0219622022500559 crossref_primary_10_1007_s00779_021_01599_0 crossref_primary_10_3390_ai5030050 crossref_primary_10_1016_j_eswa_2012_10_035 crossref_primary_10_1016_j_knosys_2012_04_021 crossref_primary_10_1016_j_aci_2014_07_002 crossref_primary_10_1142_S0219649212500116 crossref_primary_10_1007_s00521_022_07950_7 crossref_primary_10_1016_j_eswa_2015_04_020 crossref_primary_10_1007_s10614_020_10016_2 crossref_primary_10_1016_j_knosys_2012_05_003 crossref_primary_10_1016_j_engappai_2016_08_012 crossref_primary_10_1007_s11831_020_09413_5 crossref_primary_10_1016_j_knosys_2016_03_025 crossref_primary_10_4028_www_scientific_net_AMM_311_81 crossref_primary_10_1142_S0219649214500270 crossref_primary_10_1109_ACCESS_2020_3045970 crossref_primary_10_1109_ACCESS_2018_2889737 crossref_primary_10_1016_j_knosys_2022_110211 crossref_primary_10_1080_09720510_2015_1086165 crossref_primary_10_1016_j_asoc_2016_09_016 crossref_primary_10_1155_2014_914641 crossref_primary_10_4018_JITR_2019010107 crossref_primary_10_1007_s00779_018_1121_x crossref_primary_10_1007_s10660_021_09467_y crossref_primary_10_1109_TASE_2023_3246590 crossref_primary_10_1016_j_eswa_2015_01_002 crossref_primary_10_1016_j_asoc_2015_07_008 crossref_primary_10_1016_j_engappai_2014_09_001 crossref_primary_10_1016_j_eswa_2012_03_036 |
| Cites_doi | 10.1145/347090.347147 10.1016/S0950-7051(02)00079-5 10.1016/j.amc.2006.03.022 10.1017/S0269888907001026 10.1016/j.knosys.2010.01.006 10.1016/j.knosys.2009.11.001 10.1145/967900.968016 10.1145/1008694.1008705 10.1137/1.9781611972733.40 10.1016/j.knosys.2005.11.001 10.1016/j.dss.2005.03.005 10.1016/j.eswa.2008.08.040 10.1016/j.knosys.2008.03.037 10.1109/ICDM.2002.1183883 10.1109/ICDM.2004.10117 10.1145/347090.347128 10.1007/978-1-4615-1733-7_30 |
| ContentType | Journal Article |
| Copyright | 2010 Elsevier B.V. |
| Copyright_xml | – notice: 2010 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.knosys.2010.04.007 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-7409 |
| EndPage | 614 |
| ExternalDocumentID | 10_1016_j_knosys_2010_04_007 S0950705110000663 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 29L 4.4 457 4G. 5VS 7-5 71M 77K 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABAOU ABBOA ABIVO ABJNI ABMAC ABXDB ABYKQ ACAZW ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADGUI ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 LY7 M41 MHUIS MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SET SEW SPC SPCBC SST SSV SSW SSZ T5K UHS WH7 WUQ XPP ZMT ~02 ~G- 77I AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c306t-36b7c77dddce4e34e7dad4ea950df572edefd9e5c75890b56fc44f2df35325153 |
| IEDL.DBID | AIKHN |
| ISSN | 0950-7051 |
| IngestDate | Thu Apr 24 23:00:29 EDT 2025 Wed Oct 01 06:54:29 EDT 2025 Fri Feb 23 02:28:22 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Keywords | Data mining Genetic algorithm Numerical data Associative classification rules |
| Language | English |
| License | https://www.elsevier.com/tdm/userlicense/1.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c306t-36b7c77dddce4e34e7dad4ea950df572edefd9e5c75890b56fc44f2df35325153 |
| PageCount | 10 |
| ParticipantIDs | crossref_primary_10_1016_j_knosys_2010_04_007 crossref_citationtrail_10_1016_j_knosys_2010_04_007 elsevier_sciencedirect_doi_10_1016_j_knosys_2010_04_007 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2010-08-01 |
| PublicationDateYYYYMMDD | 2010-08-01 |
| PublicationDate_xml | – month: 08 year: 2010 text: 2010-08-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationTitle | Knowledge-based systems |
| PublicationYear | 2010 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | C. Merz, P. Murphy, UCI Repository of Machine Learning Databases, University of California, Department of Information and Computer Science, Irvine, CA, 1996. H.Y. Liu, J. Chen, G. Chen, Mining insightful classification rules directly and efficiently, in: Proceedings of the 1999 IEEE International Conference on Systems Man and Cybernetics, IEEE Computer Society, Tokyo, 1999, pp. 911–916. Hu, Chen, Tzeng (bib14) 2003; 16 Lim, Lee (bib18) 2010; 23 Chang Chien, Chen (bib8) 2009; 36 F. Thabtah, P. Cowling, Y. Peng, MCAR: Multi-class classification based on association rule approach, in: Proceedings of the 3rd IEEE International Conference on Computer Systems and Applications, Cairo, Egypt, 2005, pp. 1–7. Bauer (bib2) 1994 Hu (bib13) 2006; 19 B. Liu, W. Hsu, Y. Ma, Integrating classification and association rule mining, in: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), New York City, USA, 1998, pp. 80–86. B. Liu. Y. Ma, C.K. Wong, Classification using association rules: weakness and enhancements, in: Vipin Kumar et al., (Eds.), Data Mining for Scientific and Engineering Application, 2001, p. 591. B. Liu, M. Hu, W. Hsu, Multi-level organization and summarization of the discovered rules, in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2000), ACM Press, Boston, 2000, pp. 208–217. Deng, He, Xu (bib10) 2010; 23 Murphy (bib25) 1991 E. Baralis, S. Chiusano, P. Graza, On support thresholds in associative classification, in: Proceedings of the 2004 ACM Symposium on Applied Computing. Nicosia, Cyprus, ACM Press, 2004, pp. 553–558. Jobman (bib15) 1995 F. Thabtah, P. Cowling, Y. Peng, MMAC: a new multi-class, multi-label associative classification approach, in: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM’04), Brighton, UK, 2004, pp. 217–224. K. Ali, S. Manganaris, R. Srikant, Partial classification using association rules, in: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, AAAI Press, Newport Beach, California, 1997, pp. 115–118. Chen, Liu, Yu, Wei, Zhang (bib9) 2006; 42 Fayyad, Piatetsky-Shaprio, Smyth (bib11) 1996 E. Baralis, P. Torino, A lazy approach to pruning classification rules, in: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM’02), Maebashi City, Japan, 2002, pp. 35–42. Kovalerchuk, Vityaev (bib16) 2000 Liu, Jiang, Liu, Yang (bib23) 2008; 21 O. Zaïane, A. Antonie, Classifying text documents by associating terms with text categories, in: Proceedings of the 13th Australasian Database Conference (ADC’02), Melbourne, Australia, 2002, pp. 215–222. X. Xu, G. Han, H. Min, A novel algorithm for associative classification of images blocks, in: Proceedings of the 4th IEEE International Conference on Computer and Information Technology, Lian, Shiguo, China, 2004, pp. 46–51. X. Yin, J. Han, CPAR: Classification based on predictive association rules, in: Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, CA, USA, 2003, pp. 208–217. Holland (bib12) 1992 W. Li, J. Han, J. Pei, CMAR: accurate and efficient classification based on multiple class-association rules, in: Proceedings of ICDM 2001, 2001, pp. 369–376. Thabath (bib27) 2007; 22 M. Antonie, O. Zaïane, An associative classifier based on positive and negative rules, in: Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, ACM Press, Paris, France, 2004, pp. 64–69. M. Antonie, O. Zaïane, A. Coman, Associative classifiers for medical images, Mining Multimedia and Complex Data, Lecture Notes in Artificial Intelligence, vol. 2797, 2003, pp. 68–83. Safaei, Sadjadi, Babakhani (bib26) 2006; 181 Achelis (bib1) 2000 K. Wang, S. Zhou, Y. He, Growing decision tree on support-less association rules, in: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, Boston, MA, 2000, pp. 265–269. 10.1016/j.knosys.2010.04.007_bib20 Murphy (10.1016/j.knosys.2010.04.007_bib25) 1991 10.1016/j.knosys.2010.04.007_bib24 Kovalerchuk (10.1016/j.knosys.2010.04.007_bib16) 2000 10.1016/j.knosys.2010.04.007_bib22 Thabath (10.1016/j.knosys.2010.04.007_bib27) 2007; 22 10.1016/j.knosys.2010.04.007_bib21 Safaei (10.1016/j.knosys.2010.04.007_bib26) 2006; 181 10.1016/j.knosys.2010.04.007_bib7 10.1016/j.knosys.2010.04.007_bib6 10.1016/j.knosys.2010.04.007_bib5 Hu (10.1016/j.knosys.2010.04.007_bib13) 2006; 19 Chang Chien (10.1016/j.knosys.2010.04.007_bib8) 2009; 36 Lim (10.1016/j.knosys.2010.04.007_bib18) 2010; 23 10.1016/j.knosys.2010.04.007_bib28 10.1016/j.knosys.2010.04.007_bib4 10.1016/j.knosys.2010.04.007_bib3 Hu (10.1016/j.knosys.2010.04.007_bib14) 2003; 16 10.1016/j.knosys.2010.04.007_bib29 10.1016/j.knosys.2010.04.007_bib31 10.1016/j.knosys.2010.04.007_bib30 Chen (10.1016/j.knosys.2010.04.007_bib9) 2006; 42 Fayyad (10.1016/j.knosys.2010.04.007_bib11) 1996 10.1016/j.knosys.2010.04.007_bib33 10.1016/j.knosys.2010.04.007_bib32 Achelis (10.1016/j.knosys.2010.04.007_bib1) 2000 Liu (10.1016/j.knosys.2010.04.007_bib23) 2008; 21 Holland (10.1016/j.knosys.2010.04.007_bib12) 1992 Jobman (10.1016/j.knosys.2010.04.007_bib15) 1995 10.1016/j.knosys.2010.04.007_bib17 Deng (10.1016/j.knosys.2010.04.007_bib10) 2010; 23 Bauer (10.1016/j.knosys.2010.04.007_bib2) 1994 10.1016/j.knosys.2010.04.007_bib19 |
| References_xml | – volume: 16 start-page: 137 year: 2003 end-page: 147 ident: bib14 article-title: Discovering fuzzy association rules using fuzzy partition methods publication-title: Knowledge-Based Systems – reference: M. Antonie, O. Zaïane, A. Coman, Associative classifiers for medical images, Mining Multimedia and Complex Data, Lecture Notes in Artificial Intelligence, vol. 2797, 2003, pp. 68–83. – reference: O. Zaïane, A. Antonie, Classifying text documents by associating terms with text categories, in: Proceedings of the 13th Australasian Database Conference (ADC’02), Melbourne, Australia, 2002, pp. 215–222. – reference: C. Merz, P. Murphy, UCI Repository of Machine Learning Databases, University of California, Department of Information and Computer Science, Irvine, CA, 1996. – reference: X. Xu, G. Han, H. Min, A novel algorithm for associative classification of images blocks, in: Proceedings of the 4th IEEE International Conference on Computer and Information Technology, Lian, Shiguo, China, 2004, pp. 46–51. – volume: 181 start-page: 1693 year: 2006 end-page: 1702 ident: bib26 article-title: An efficient genetic algorithm for determining the optimal price discrimination publication-title: Applied Mathematics and Computation – reference: F. Thabtah, P. Cowling, Y. Peng, MMAC: a new multi-class, multi-label associative classification approach, in: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM’04), Brighton, UK, 2004, pp. 217–224. – reference: K. Wang, S. Zhou, Y. He, Growing decision tree on support-less association rules, in: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, Boston, MA, 2000, pp. 265–269. – reference: F. Thabtah, P. Cowling, Y. Peng, MCAR: Multi-class classification based on association rule approach, in: Proceedings of the 3rd IEEE International Conference on Computer Systems and Applications, Cairo, Egypt, 2005, pp. 1–7. – reference: B. Liu, W. Hsu, Y. Ma, Integrating classification and association rule mining, in: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), New York City, USA, 1998, pp. 80–86. – reference: M. Antonie, O. Zaïane, An associative classifier based on positive and negative rules, in: Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, ACM Press, Paris, France, 2004, pp. 64–69. – volume: 42 start-page: 674 year: 2006 end-page: 689 ident: bib9 article-title: A new approach to classification based on association rule mining publication-title: Decision Support Systems – volume: 23 start-page: 144 year: 2010 end-page: 149 ident: bib10 article-title: G-ANMI: a mutual information based genetic clustering algorithm for categorical data publication-title: Knowledge-Based Systems – reference: W. Li, J. Han, J. Pei, CMAR: accurate and efficient classification based on multiple class-association rules, in: Proceedings of ICDM 2001, 2001, pp. 369–376. – reference: E. Baralis, S. Chiusano, P. Graza, On support thresholds in associative classification, in: Proceedings of the 2004 ACM Symposium on Applied Computing. Nicosia, Cyprus, ACM Press, 2004, pp. 553–558. – reference: B. Liu, M. Hu, W. Hsu, Multi-level organization and summarization of the discovered rules, in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2000), ACM Press, Boston, 2000, pp. 208–217. – year: 1991 ident: bib25 article-title: Intermarket Technical Analysis: Trading Strategies for the Global Stock, Bond, Commodity, and Currency Markets – volume: 23 start-page: 248 year: 2010 end-page: 255 ident: bib18 article-title: Processing online analytics with classification and association rule mining publication-title: Knowledge-Based Systems – volume: 21 start-page: 786 year: 2008 end-page: 793 ident: bib23 article-title: CSMC: a combination strategy for multi-class classification based on multiple association rules publication-title: Knowledge-Based Systems – year: 2000 ident: bib1 article-title: Technical Analysis from A to Z – start-page: 1 year: 1996 end-page: 35 ident: bib11 article-title: From data mining to knowledge discovery: an overview publication-title: Advanced in Knowledge Discovery and Data Mining – volume: 36 start-page: 6935 year: 2009 end-page: 6944 ident: bib8 article-title: A phenotypic genetic algorithm for inductive logic programming publication-title: Expert Systems with Applications – year: 1994 ident: bib2 article-title: Genetic Algorithms and Investment Strategies – reference: E. Baralis, P. Torino, A lazy approach to pruning classification rules, in: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM’02), Maebashi City, Japan, 2002, pp. 35–42. – year: 1995 ident: bib15 article-title: The Handbook of Technical Analysis: A Comprehensive Guide to Analytical Methods, Trading Systems and Technical Indicators – reference: K. Ali, S. Manganaris, R. Srikant, Partial classification using association rules, in: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, AAAI Press, Newport Beach, California, 1997, pp. 115–118. – reference: X. Yin, J. Han, CPAR: Classification based on predictive association rules, in: Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, CA, USA, 2003, pp. 208–217. – reference: B. Liu. Y. Ma, C.K. Wong, Classification using association rules: weakness and enhancements, in: Vipin Kumar et al., (Eds.), Data Mining for Scientific and Engineering Application, 2001, p. 591. – year: 2000 ident: bib16 article-title: Data Mining in Finance: Advanced in Relational and Hybrid Methods – volume: 22 start-page: 37 year: 2007 end-page: 65 ident: bib27 article-title: A review of associative classification mining publication-title: Knowledge Engineering Review – reference: H.Y. Liu, J. Chen, G. Chen, Mining insightful classification rules directly and efficiently, in: Proceedings of the 1999 IEEE International Conference on Systems Man and Cybernetics, IEEE Computer Society, Tokyo, 1999, pp. 911–916. – volume: 19 start-page: 57 year: 2006 end-page: 66 ident: bib13 article-title: Determining membership functions and minimum fuzzy support in finding association rules for classification problems publication-title: Knowledge-Based Systems – year: 1992 ident: bib12 article-title: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence – start-page: 1 year: 1996 ident: 10.1016/j.knosys.2010.04.007_bib11 article-title: From data mining to knowledge discovery: an overview – ident: 10.1016/j.knosys.2010.04.007_bib30 doi: 10.1145/347090.347147 – volume: 16 start-page: 137 issue: 3 year: 2003 ident: 10.1016/j.knosys.2010.04.007_bib14 article-title: Discovering fuzzy association rules using fuzzy partition methods publication-title: Knowledge-Based Systems doi: 10.1016/S0950-7051(02)00079-5 – volume: 181 start-page: 1693 year: 2006 ident: 10.1016/j.knosys.2010.04.007_bib26 article-title: An efficient genetic algorithm for determining the optimal price discrimination publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2006.03.022 – year: 2000 ident: 10.1016/j.knosys.2010.04.007_bib16 – ident: 10.1016/j.knosys.2010.04.007_bib24 – volume: 22 start-page: 37 issue: 1 year: 2007 ident: 10.1016/j.knosys.2010.04.007_bib27 article-title: A review of associative classification mining publication-title: Knowledge Engineering Review doi: 10.1017/S0269888907001026 – volume: 23 start-page: 248 issue: 3 year: 2010 ident: 10.1016/j.knosys.2010.04.007_bib18 article-title: Processing online analytics with classification and association rule mining publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2010.01.006 – year: 1995 ident: 10.1016/j.knosys.2010.04.007_bib15 – volume: 23 start-page: 144 issue: 2 year: 2010 ident: 10.1016/j.knosys.2010.04.007_bib10 article-title: G-ANMI: a mutual information based genetic clustering algorithm for categorical data publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2009.11.001 – ident: 10.1016/j.knosys.2010.04.007_bib7 doi: 10.1145/967900.968016 – ident: 10.1016/j.knosys.2010.04.007_bib4 doi: 10.1145/1008694.1008705 – year: 1994 ident: 10.1016/j.knosys.2010.04.007_bib2 – ident: 10.1016/j.knosys.2010.04.007_bib33 – ident: 10.1016/j.knosys.2010.04.007_bib31 – ident: 10.1016/j.knosys.2010.04.007_bib32 doi: 10.1137/1.9781611972733.40 – volume: 19 start-page: 57 issue: 1 year: 2006 ident: 10.1016/j.knosys.2010.04.007_bib13 article-title: Determining membership functions and minimum fuzzy support in finding association rules for classification problems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2005.11.001 – volume: 42 start-page: 674 year: 2006 ident: 10.1016/j.knosys.2010.04.007_bib9 article-title: A new approach to classification based on association rule mining publication-title: Decision Support Systems doi: 10.1016/j.dss.2005.03.005 – ident: 10.1016/j.knosys.2010.04.007_bib29 – ident: 10.1016/j.knosys.2010.04.007_bib3 – year: 2000 ident: 10.1016/j.knosys.2010.04.007_bib1 – ident: 10.1016/j.knosys.2010.04.007_bib5 – ident: 10.1016/j.knosys.2010.04.007_bib22 – year: 1992 ident: 10.1016/j.knosys.2010.04.007_bib12 – volume: 36 start-page: 6935 issue: 3 year: 2009 ident: 10.1016/j.knosys.2010.04.007_bib8 article-title: A phenotypic genetic algorithm for inductive logic programming publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.08.040 – volume: 21 start-page: 786 issue: 8 year: 2008 ident: 10.1016/j.knosys.2010.04.007_bib23 article-title: CSMC: a combination strategy for multi-class classification based on multiple association rules publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2008.03.037 – ident: 10.1016/j.knosys.2010.04.007_bib6 doi: 10.1109/ICDM.2002.1183883 – ident: 10.1016/j.knosys.2010.04.007_bib28 doi: 10.1109/ICDM.2004.10117 – ident: 10.1016/j.knosys.2010.04.007_bib19 – ident: 10.1016/j.knosys.2010.04.007_bib20 doi: 10.1145/347090.347128 – ident: 10.1016/j.knosys.2010.04.007_bib17 – ident: 10.1016/j.knosys.2010.04.007_bib21 doi: 10.1007/978-1-4615-1733-7_30 – year: 1991 ident: 10.1016/j.knosys.2010.04.007_bib25 |
| SSID | ssj0002218 |
| Score | 2.208688 |
| Snippet | Associative classifiers are a classification system based on associative classification rules. Although associative classification is more accurate than a... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 605 |
| SubjectTerms | Associative classification rules Data mining Genetic algorithm Numerical data |
| Title | Mining associative classification rules with stock trading data – A GA-based method |
| URI | https://dx.doi.org/10.1016/j.knosys.2010.04.007 |
| Volume | 23 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier Science Direct Journals customDbUrl: eissn: 1872-7409 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002218 issn: 0950-7051 databaseCode: ACRLP dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier ScienceDirect customDbUrl: eissn: 1872-7409 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002218 issn: 0950-7051 databaseCode: .~1 dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: ScienceDirect Freedom Collection customDbUrl: eissn: 1872-7409 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002218 issn: 0950-7051 databaseCode: AIKHN dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVLSH databaseName: Elsevier Journals customDbUrl: mediaType: online eissn: 1872-7409 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002218 issn: 0950-7051 databaseCode: AKRWK dateStart: 19871201 isFulltext: true providerName: Library Specific Holdings |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1NSwMxEB3aevHit_hZcvAau80mze6xiLUqetGCtyWbZKFWWmmr4EX8D_5Df4mTbLYoiILXJQO7b7Pz3g4vMwBHuUKRLUxEDZIHdR3paJ4oRblgUguJAtyXLq6uO_0Bv7gTdzU4qc7COFtlyP1lTvfZOlxpBTRbj8Nh6wbFAe5X0fYVaiTOOiwh_yRJA5a655f960VCZsyX-dx66gKqE3Te5jUaT2Yvs-Dx8p20f2aoL6zTW4OVIBdJt7yjdajZ8QasVqMYSPgyN2Fw5Qc9EFWh_WyJdsLYOYE8-GT69GBnxNVdCQo-PSLzqffPE2cSJR9v76RLzrrU0Zoh5WDpLRj0Tm9P-jRMTKAapf-cxp1caimNMdpyG3MrjTLcKnxyUwjJrLGFSa3Q-JeQRrnoFJrzgpkiFjEKHRFvQ2M8GdsdIHnCU-QtxVhU8FSxPFaSJcpEBjnNqmIX4gqlTId24m6qxUNW-cbusxLbzGGbRTxDbHeBLqIey3Yaf6yX1QvIvm2LDDP-r5F7_47ch-XSIuBcfgfQmE-f7CEqj3nehPrxa7sZ9tcnq6fZ9Q |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1NT8IwGG4QD3rx24ifPXitzK6l7EiIiApchIRb07VdgpBBYJh4Mf4H_6G_xLbr_EiMJl6XNtuede_z9M3T9wXgPBZGZFMVIGXIA9mKdCiuC4EIxUxSZgS4S110e7X2gNwO6bAEmsVZGGur9LE_j-kuWvsrVY9mdTYaVe-NODDrlV66DLUhzhWwau9gd2AXz58-D4xdks-ORnZ4cX7OmbzG6XTxtPAOL1dH-2d--sI5rS2w4cUibOTPsw1KOt0Bm0UjBuj_y10w6Lo2D1AUWD9qKK0stj4gBz2cLyd6AW3WFRq5J8cwmzv3PLQWUfj28gob8LqBLKkpmLeV3gOD1lW_2Ua-XwKSRvhnKKzFTDKmlJKa6JBopoQiWpg3VwllWCudqEhTafYIURDTWiIJSbBKQhoamUPDfVBOp6k-ADCuk8iwlsA4SEgkcBwKhutCBcowmhZJBYQFSlz6YuK2p8WEF66xB55jyy22PCDcYFsB6GPWLC-m8cd4VnwA_m1RcBPvf515-O-ZZ2Ct3e92eOemd3cE1nOzgPX7HYNyNl_qE6NBsvjUrbF3fePavQ |
| 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=Mining+associative+classification+rules+with+stock+trading+data+%E2%80%93+A+GA-based+method&rft.jtitle=Knowledge-based+systems&rft.au=Chang+Chien%2C+Ya-Wen&rft.au=Chen%2C+Yen-Liang&rft.date=2010-08-01&rft.pub=Elsevier+B.V&rft.issn=0950-7051&rft.eissn=1872-7409&rft.volume=23&rft.issue=6&rft.spage=605&rft.epage=614&rft_id=info:doi/10.1016%2Fj.knosys.2010.04.007&rft.externalDocID=S0950705110000663 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0950-7051&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0950-7051&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0950-7051&client=summon |