메뉴 건너뛰기




Volumn 8, Issue 1, 2008, Pages 646-656

MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules

Author keywords

Data mining; Differential evolution; Evolutionary computation; Machine learning; Multi objective optimization

Indexed keywords

ASSOCIATION RULES; DATA MINING; DATABASE SYSTEMS; EVOLUTIONARY ALGORITHMS; LEARNING SYSTEMS; PARETO PRINCIPLE;

EID: 34548472851     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2007.05.003     Document Type: Article
Times cited : (191)

References (33)
  • 2
    • 0033318858 scopus 로고    scopus 로고
    • Multi-objective evolutionary algorithms: a comparative case study and the strength pareto approach
    • Zitzler E., and Thiele L. Multi-objective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3 4 (1999) 257-271
    • (1999) IEEE Trans. Evol. Comput. , vol.3 , Issue.4 , pp. 257-271
    • Zitzler, E.1    Thiele, L.2
  • 4
    • 0027621699 scopus 로고
    • Mining association rules between sets of items in large databases
    • Washington, DC
    • Agrawal R., Imielinski T., and Swami A.N. Mining association rules between sets of items in large databases. Proceedings of ACM SIGMOD. Washington, DC (1993) 207-216
    • (1993) Proceedings of ACM SIGMOD , pp. 207-216
    • Agrawal, R.1    Imielinski, T.2    Swami, A.N.3
  • 5
    • 33749645082 scopus 로고    scopus 로고
    • MIC framework: an information-theoretic approach to quantitative association rule mining
    • Ke K., Cheng J., and Ng W. MIC framework: an information-theoretic approach to quantitative association rule mining. Proceedings of the ICDE '06 (2006) 112-114
    • (2006) Proceedings of the ICDE '06 , pp. 112-114
    • Ke, K.1    Cheng, J.2    Ng, W.3
  • 6
    • 0030157416 scopus 로고    scopus 로고
    • Mining quantitative association rules in large relational tables
    • Srikant R., and Agrawal R. Mining quantitative association rules in large relational tables. Proceedings of the ACMSIGMOD (1996) 1-12
    • (1996) Proceedings of the ACMSIGMOD , pp. 1-12
    • Srikant, R.1    Agrawal, R.2
  • 10
    • 0038005421 scopus 로고    scopus 로고
    • A statistical theory for quantitative association rules
    • Aumann Y., and Lindell Y. A statistical theory for quantitative association rules. J. Intell. Inf. Syst. 20 3 (2003) 255-283
    • (2003) J. Intell. Inf. Syst. , vol.20 , Issue.3 , pp. 255-283
    • Aumann, Y.1    Lindell, Y.2
  • 14
    • 34548515804 scopus 로고    scopus 로고
    • A novel approach based on genetic algorithm and fuzzy logic for mining of association rules
    • in turkish
    • Alatas B., and Arslan A. A novel approach based on genetic algorithm and fuzzy logic for mining of association rules. J. Sci. Eng. (Firat University) 17 1 (2005) 42-51 in turkish
    • (2005) J. Sci. Eng. (Firat University) , vol.17 , Issue.1 , pp. 42-51
    • Alatas, B.1    Arslan, A.2
  • 15
    • 34548511871 scopus 로고    scopus 로고
    • Mining of fuzzy association rules with genetic algorithms
    • in turkish
    • Alatas B., and Arslan A. Mining of fuzzy association rules with genetic algorithms. J. Polytech. (Gazi University) 7 4 (2004) 269-276 in turkish
    • (2004) J. Polytech. (Gazi University) , vol.7 , Issue.4 , pp. 269-276
    • Alatas, B.1    Arslan, A.2
  • 16
    • 17644408058 scopus 로고    scopus 로고
    • Genetic algorithm based framework for mining fuzzy association rules
    • Kaya M., and Alhajj R. Genetic algorithm based framework for mining fuzzy association rules. Fuzzy Sets Syst. 152 3 (2005) 587-601
    • (2005) Fuzzy Sets Syst. , vol.152 , Issue.3 , pp. 587-601
    • Kaya, M.1    Alhajj, R.2
  • 18
    • 29444447081 scopus 로고    scopus 로고
    • An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules
    • Springer-Verlag pp. 230-237
    • Alatas B., and Akin E. An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules. Soft Computing, vol 10 no. 3 (2006), Springer-Verlag pp. 230-237
    • (2006) Soft Computing, vol 10 no. 3
    • Alatas, B.1    Akin, E.2
  • 19
    • 2442699339 scopus 로고    scopus 로고
    • Multi-objective rule mining using genetic algorithms
    • Elsevier Inc. pp. 123-133
    • Ghosh A., and Nath B. Multi-objective rule mining using genetic algorithms. Information Sciences, vol. 163, no. 1-3 (2004), Elsevier Inc. pp. 123-133
    • (2004) Information Sciences, vol. 163, no. 1-3
    • Ghosh, A.1    Nath, B.2
  • 20
    • 34548499604 scopus 로고    scopus 로고
    • R. Storn, K. Price, Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012, ICSI, 1995.
  • 21
    • 3142742523 scopus 로고    scopus 로고
    • A simple and global optimization algorithm for engineering problems: differential evolution algorithm
    • Karaboga D., and Okdem S. A simple and global optimization algorithm for engineering problems: differential evolution algorithm. Turkish J. Electr. Eng. Comput. Sci. 12 1 (2004) 53-60
    • (2004) Turkish J. Electr. Eng. Comput. Sci. , vol.12 , Issue.1 , pp. 53-60
    • Karaboga, D.1    Okdem, S.2
  • 23
    • 34548492574 scopus 로고    scopus 로고
    • S. Baluja, Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning, Technical Report CMU-CS-94-163, Comp. Sci. Dep., Carnegie Mellon University, 1994.
  • 24
    • 34548514056 scopus 로고    scopus 로고
    • I. Rechenberg, Evolution Strategy, in Zuarda et.al. (1994) 147-159.
  • 25
    • 34548472044 scopus 로고    scopus 로고
    • Storn, R., On the Use of differential evolution for function optimization, Technical Report, ICSI, Berkeley, 1996.
  • 26
    • 34548488314 scopus 로고    scopus 로고
    • R. Pérez-Guerrero, Differential evolution based power dispatch algorithms, Master Thesis, University of Puerto Rico, 2004.
  • 27
    • 0000626184 scopus 로고    scopus 로고
    • An updated survey of GA-based multi-objective optimization techniques
    • Coello C.A. An updated survey of GA-based multi-objective optimization techniques. ACM Comput. Surveys 32 2 (2000) 109-143
    • (2000) ACM Comput. Surveys , vol.32 , Issue.2 , pp. 109-143
    • Coello, C.A.1
  • 30
    • 4444269086 scopus 로고    scopus 로고
    • Differential evolution for solving multi-objective optimization problems
    • Sarker R., and Abbass H.A. Differential evolution for solving multi-objective optimization problems. Asia-Pacific J. Operat. Res. 21 2 (2004) 225-240
    • (2004) Asia-Pacific J. Operat. Res. , vol.21 , Issue.2 , pp. 225-240
    • Sarker, R.1    Abbass, H.A.2
  • 31
    • 0037700359 scopus 로고    scopus 로고
    • Mixed integer-discrete-continuous optimization by differential evolution. Part 1. The optimization method
    • Ošmera P. (Ed). Brno, Czech Republic
    • Lampinen J., and Zelinka I. Mixed integer-discrete-continuous optimization by differential evolution. Part 1. The optimization method. In: Ošmera P. (Ed). Proceedings of MENDEL'99, fifth International Mendel Conference on Soft Computing. Brno, Czech Republic (1999) 71-76
    • (1999) Proceedings of MENDEL'99, fifth International Mendel Conference on Soft Computing , pp. 71-76
    • Lampinen, J.1    Zelinka, I.2
  • 32
    • 0034876065 scopus 로고    scopus 로고
    • A Pareto-frontier differential evolution approach for multi-objective optimization problems
    • Seoul, South Korea, IEEE, Piscataway, NJ, USA
    • Abbass H.A., Sarker R., Newton C., and PDE. A Pareto-frontier differential evolution approach for multi-objective optimization problems. Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2. Seoul, South Korea (2001), IEEE, Piscataway, NJ, USA 971-L978
    • (2001) Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2
    • Abbass, H.A.1    Sarker, R.2    Newton, C.3    PDE4
  • 33
    • 34548514900 scopus 로고    scopus 로고
    • H. A. Guvenir, I. Uysal, Bilkent University Function Approximation Repository, 2000 http://funapp.cs.bilkent.edu.tr.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.