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Genetic-Fuzzy Data Mining Techniques

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Encyclopedia of Complexity and Systems Science

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Definition of the Subject

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Mostconventional data‐mining algorithms identify the relationships among transactions using binary values. However, transactions with quantitativevalues are commonly seen in real-world applications. Fuzzy data‐mining algorithms are thus proposed for extracting interesting linguistic knowledge from transactions stored as quantitative values. Theyusually integrate fuzzy-set concepts and mining algorithms to find interesting fuzzy knowledge from a given transaction data set. Most of them minefuzzy knowledge under the assumption that a set of membership functions [8,23,24,35,36,50] is knownin advance for the problem to be solved. The given membership functions may, however, have a critical influence on the final miningresults. Different membership functions may infer different knowledge. Automatically deriving an appropriate set of...

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Abbreviations

Data mining:

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. The common techniques include mining association rules, mining sequential patterns, clustering, and classification, among others.

Fuzzy set theory:

The fuzzy set theory was first proposed by Zadeh in 1965. It is primarily concerned with quantifying and reasoning using natural language in which words can have ambiguous meanings. It is widely used in a variety of fields because of its simplicity and similarity to human reasoning.

Fuzzy data mining:

The concept of fuzzy sets can be used in data mining to handle quantitative or linguistic data. Basically, fuzzy data mining first uses membership functions to transform each quantitative value into a fuzzy set in linguistic terms and then uses a fuzzy mining process to find fuzzy association rules.

Genetic algorithms:

Genetic Algorithms (GAs) were first proposed by Holland in 1975. They have become increasingly important for researchers in solving difficult problems since they could provide feasible solutions in a limited amount of time. Each possible solution is encoded as a chromosome (individual) in a population. According to the principle of survival of the fittest, GAs generate the next population by several genetic operations such as crossover, mutation, and reproductions.

Genetic‐fuzzy data mining:

Genetic algorithms have been widely used for solving optimization problems. If the fuzzy mining problem can be converted into an optimization problem, then the GA techniques can easily be adopted to solve it. They are thus called genetic‐fuzzy data‐mining techniques. They are usually used to automatically mine both appropriate membership functions and fuzzy association rules from a set of transaction data.

Bibliography

  1. Agrawal R, Srikant R (1994) Fast algorithm for mining association rules. In:Proceedings of the international conference on very large data bases, pp 487–499

    Google Scholar 

  2. Agrawal R, Imielinksi T, Swami A (1993) Database mining: a performanceperspective. Trans IEEE Knowl Data Eng 5(6):914–925

    Google Scholar 

  3. Agrawal R, Imielinksi T, Swami A (1993) Mining association rules between sets ofitems in large database. In: Proceedings of the conference ACMSIGMOD, Washington DC, USA

    Google Scholar 

  4. Agrawal R, Srikant R, Vu Q (1997) Mining association rules with itemconstraints. In: Proceedings of the third international conference on knowledge discovery in databases and data mining, Newport Beach, California, August1997

    Google Scholar 

  5. Au WH, Chan KCC, Yao X (2003) A novel evolutionary data mining algorithmwith applications to churn prediction. Trans IEEE Evol Comput 7(6):532–545

    Google Scholar 

  6. Aumann Y, Lindell Y (1999) A statistical theory for quantitativeassociation rules. In: Proceedings of the ACMSIGKDD international conference on knowledge discovery and data mining,pp 261–270

    Google Scholar 

  7. Casillas J, Cordón O, del Jesus MJ, Herrera F (2005) Genetic tuning offuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. Trans IEEE Fuzzy Syst13(1):13–29

    Google Scholar 

  8. Chan CC, Au WH (1997) Mining fuzzy association rules. In: Proceedings of theconference on information and knowledge management, Las Vegas, pp 209–215

    Google Scholar 

  9. Chen CH, Hong TP, Vincent Tseng S (2007) A comparison of different fitnessfunctions for extracting membership functions used in fuzzy data mining. In: Proceedings of the symposium IEEE on foundations of computationalintelligence, pp 550–555

    Google Scholar 

  10. Chen CH, Hong TP, Tseng VS (2007) A modified approach to speed upgenetic‐fuzzy data mining with divide‐and‐conquer strategy. In: Proceedings of the congress IEEE on evolutionary computation (CEC),pp 1–6

    Google Scholar 

  11. Chen CH, Tseng VS, Hong TP (2008) Cluster‐based evaluation infuzzy‐genetic data mining. Trans IEEE Fuzzy Syst 16(1):249–262

    Google Scholar 

  12. Chen CH, Hong TP, Tseng VS, Lee CS (2008) A genetic‐fuzzy miningapproach for items with multiple minimum supports. Accepted and to appear in Soft Computing (SCI)

    Google Scholar 

  13. Chen J, Mikulcic A, Kraft DH (2000) An integrated approach to informationretrieval with fuzzy clustering and fuzzy inferencing. In: Pons O, Vila MA, Kacprzyk J (eds) Knowledge management in fuzzy databases. Physica,Heidelberg

    Google Scholar 

  14. Cordón O, Herrera F, Villar P (2001) Generating the knowledge base ofa fuzzy rule-based system by the genetic learning of the data base. Trans IEEE Fuzzy Syst 9(4):667–674

    Google Scholar 

  15. Darwen PJ, Yao X (1997) Speciation as automatic categoricalmodularization. Trans IEEE Evol Comput 1(2):101–108

    Google Scholar 

  16. Frawley WJ, Piatetsky‐Shapiro G, Matheus CJ (1991) Knowledge discoveryin databases: an overview. In: Proceedings of the workshop AAAI on knowledge discovery in databases, pp 1–27

    Google Scholar 

  17. Goldberg DE (1989) Genetic algorithms in search, optimization and machinelearning. Addison Wesley, Boston

    MATH  Google Scholar 

  18. Grefenstette JJ (1986) Optimization of control parameters for geneticalgorithms. Trans IEEE Syst Man Cybern 16(1):122–128

    Google Scholar 

  19. Heng PA, Wong TT, Rong Y, Chui YP, Xie YM, Leung KS, Leung PC (2006)Intelligent inferencing and haptic simulation for Chinese acupuncture learning and training. Trans IEEE Inf Technol Biomed10(1):28–41

    Google Scholar 

  20. Holland JH (1975) Adaptation in natural and artificial systems. University ofMichigan Press, Michigan

    Google Scholar 

  21. Homaifar A, Guan S, Liepins GE (1993) A new approach on the travelingsalesman problem by genetic algorithms. In: Proceedings of the fifth international conference on genetic algorithms

    Google Scholar 

  22. Hong TP, Lee YC (2001) Mining coverage‐based fuzzy rules by evolutionalcomputation. In: Proceedings of the international IEEE conference on data mining, pp 218–224

    Google Scholar 

  23. Hong TP, Kuo CS, Chi SC (1999) Mining association rules from quantitativedata. Intell Data Anal 3(5):363–376

    MATH  Google Scholar 

  24. Hong TP, Kuo CS, Chi SC (2001) Trade-off between time complexity and number ofrules for fuzzy mining from quantitative data. Int J Uncertain Fuzziness Knowledge‐Based Syst 9(5):587–604

    MATH  Google Scholar 

  25. Hong TP, Chen CH, Wu YL, Tseng VS (2004) Finding active membership functionsin fuzzy data mining. In: Proceedings of the workshop on foundations of data mining in the fourth international IEEE conference on datamining

    Google Scholar 

  26. Hong TP, Chen CH, Wu YL, Lee YC (2006) AGA-based fuzzy mining approach toachieve a trade-off between number of rules and suitability of membership functions. Soft Comput 10(11):1091–1101

    Google Scholar 

  27. Hong TP, Chen CH, Wu YL, Lee YC (2008) Genetic‐fuzzy data mining withdivide‐and‐conquer strategy. Trans IEEE Evol Comput 12(2):252–265

    Google Scholar 

  28. Ishibuchi H, Yamamoto T (2004) Fuzzy rule selection by multi-objective geneticlocal search algorithms and rule evaluation measures in data mining. Fuzzy Sets Syst 141:59–88

    MathSciNet  MATH  Google Scholar 

  29. Ishibuchi H, Yamamoto T (2005) Rule weight specification in fuzzy rule-basedclassification systems. Trans IEEE Fuzzy Syst 13(4):428–435

    Google Scholar 

  30. Jin Y (2006) Multi-objective machine learning. Springer,Berlin

    MATH  Google Scholar 

  31. Kaya M, Alhajj R (2003) A clustering algorithm with genetically optimizedmembership functions for fuzzy association rules mining. In: Proceedings of the international IEEE conference on fuzzy systems,pp 881–886

    Google Scholar 

  32. Kaya M, Alhaji R (2004) Genetic algorithms based optimization of membershipfunctions for fuzzy weighted association rules mining. In: Proceedings of the international symposium on computers and communications, vol 1,pp 110–115

    Google Scholar 

  33. Kaya M, Alhajj R (2004) Integrating multi-objective genetic algorithms intoclustering for fuzzy association rules mining. In: Proceedings of the fourth international IEEE conference on data mining,pp 431–434

    Google Scholar 

  34. Khare VR, Yao X, Sendhoff B, Jin Y, Wersing H (2005) Co-evolutionary modularneural networks for automatic problem decomposition. In: Proceedings of the (2005) congress IEEE on evolutionary computation, vol 3,pp 2691–2698

    Google Scholar 

  35. Kuok C, Fu A, Wong M (1998) Mining fuzzy association rules in databases,Record SIGMOD 27(1):41–46

    Google Scholar 

  36. Lee YC, Hong TP, Lin WY (2004) Mining fuzzy association rules with multipleminimum supports using maximum constraints. In: Lecture notes in computer science, vol 3214. Springer, Heidelberg,pp 1283–1290

    Google Scholar 

  37. Liang H, Wu Z, Wu Q (2002) A fuzzy based supply chain management decisionsupport system. In: Proceedings of the world congress on intelligent control and automation, vol 4, pp 2617–2621

    Google Scholar 

  38. Mamdani EH (1974) Applications of fuzzy algorithms for control of simpledynamic plants. Proc IEEE 121(12):1585–1588

    Google Scholar 

  39. Michalewicz Z (1994) Genetic algorithms + data structures = evolutionprograms. Springer, New York

    MATH  Google Scholar 

  40. Mitchell M (1996) An introduction to genetic algorithms. MIT Press, CambridgeMA

    Google Scholar 

  41. Rasmani KA, Shen Q (2004) Modifying weighted fuzzy subsethood‐based rulemodels with fuzzy quantifiers. In: Proceedings of the international IEEE conference on fuzzy systems, vol 3,pp 1679–1684

    Google Scholar 

  42. Roubos H, Setnes M (2001) Compact and transparent fuzzy models and classifiersthrough iterative complexity reduction. Trans IEEE Fuzzy Syst 9(4):516–524

    Google Scholar 

  43. Sanchez E et al (1997) Genetic algorithms and fuzzy logic systems: softcomputing perspectives (advances in fuzzy systems – applications and theory, vol 7). World-Scientific, River Edge

    Google Scholar 

  44. Setnes M, Roubos H (2000) GA-fuzzy modeling and classification: complexity andperformance. Trans IEEE Fuzzy Syst 8(5):509–522

    Google Scholar 

  45. Siler W, James J (2004) Fuzzy expert systems and fuzzy reasoning. Wiley, NewYork

    Google Scholar 

  46. Srikant R, Agrawal R (1996) Mining quantitative association rules in largerelational tables. In: Proceedings of the (1996) international ACMSIGMOD conference on management of data, Montreal, Canada, June 1996,pp 1–12

    Google Scholar 

  47. Wang CH, Hong TP, Tseng SS (1998) Integrating fuzzy knowledge by geneticalgorithms. Trans IEEE Evol Comput 2(4):138–149

    Google Scholar 

  48. Wang CH, Hong TP, Tseng SS (2000) Integrating membership functions and fuzzyrule sets from multiple knowledge sources. Fuzzy Sets Syst 112:141–154

    Google Scholar 

  49. Yao X (2003) Adaptive divide‐and‐conquer using populations andensembles. In: Proceedings of the (2003) international conference on machine learning and application, pp 13–20

    Google Scholar 

  50. Yue S, Tsang E, Yeung D, Shi D (2000) Mining fuzzy association rules withweighted items. In: Proceedings of the international IEEE conference on systems, man and cybernetics, pp 1906–1911

    Google Scholar 

  51. Zadeh LA (1965) Fuzzy set. Inf Control8(3):338–353

    MathSciNet  MATH  Google Scholar 

  52. Zhang H, Liu D (2006) Fuzzy modeling and fuzzy control. Springer, NewYork

    MATH  Google Scholar 

  53. Zhang Z, Lu Y, Zhang B (1997) An effective partitioning‐combiningalgorithm for discovering quantitative association rules. In: Proceedings of the Pacific‐Asia conference on knowledge discovery and data mining,pp 261–270

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  2. Department of Computer Science and Information Engineering, National Cheng–Kung University, Tainan, Taiwan

    Chun-Hao Chen & Vincent S. Tseng

Authors
  1. Tzung-Pei Hong
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  2. Chun-Hao Chen
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  3. Vincent S. Tseng
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Editor information

Editors and Affiliations

  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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© 2009 Springer-Verlag

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Hong, TP., Chen, CH., Tseng, V.S. (2009). Genetic-Fuzzy Data Mining Techniques. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_244

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