Soft Computing

, Volume 15, Issue 12, pp 2319–2333 | Cite as

Genetic-fuzzy mining with multiple minimum supports based on fuzzy clustering

  • Chun-Hao Chen
  • Tzung-Pei Hong
  • Vincent S. Tseng


Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most of the previous approaches set a single minimum support threshold for all the items and identify the relationships among transactions using binary values. In real applications, different items may have different criteria to judge their importance. In the past, we proposed an algorithm for extracting appropriate multiple minimum support values, membership functions and fuzzy association rules from quantitative transactions. It used requirement satisfaction and suitability of membership functions to evaluate fitness values of chromosomes. The calculation for requirement satisfaction might take a lot of time, especially when the database to be scanned could not be totally fed into main memory. In this paper, an enhanced approach, called the fuzzy cluster-based genetic-fuzzy mining approach for items with multiple minimum supports (FCGFMMS), is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It divides the chromosomes in a population into several clusters by the fuzzy k-means clustering approach and evaluates each individual according to both their cluster and their own information. Experimental results also show the effectiveness and the efficiency of the proposed approach.


Data mining Fuzzy set Genetic algorithm Genetic-fuzzy mining Fuzzy k-means Clustering Multiple minimum supports 



This research was supported by the National Science Council of the Republic of China under contract NSC 98-2221-E-390-033.


  1. Agrawal R, Srikant R (1994) Fast algorithm for mining association rules. In: The international conference on very large databases, pp 487–499Google Scholar
  2. Alcala-Fdez J, Alcala R, Gacto M, Herrera F (2009) Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets Syst 160(7):905–921MathSciNetzbMATHCrossRefGoogle Scholar
  3. Ben-Dor A, Shamir R, Yakhini Z (1999) Clustering gene expression patterns. In: The annual international conference on computational molecular biology, pp 281–297Google Scholar
  4. Casillas J, Carse B (2009) Genetic fuzzy systems: recent developments and future directions. Soft Comput 13(3):417–418CrossRefGoogle Scholar
  5. Casillas J, Cordon O, del Jesus MJ, Herrera F (2005) Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Trans Fuzzy Syst 13(1):13–29CrossRefGoogle Scholar
  6. Chan CC, Au WH (1997) Mining fuzzy association rules. In: The conference on information and knowledge management, Las Vegas, pp 209–215Google Scholar
  7. Chen J, Mikulcic A, Kraft DH (2000) An integrated approach to information retrieval with fuzzy clustering and fuzzy inferencing. In: Pons O, Vila MA, Kacprzyk J (eds) Knowledge management in fuzzy databases. Physica-Verlag, HeidelbergGoogle Scholar
  8. Chen CH, Tseng VS, Hong TP (2008) Cluster-based evaluation in fuzzy-genetic data mining. IEEE Trans Fuzzy Syst 16(1):249–262CrossRefGoogle Scholar
  9. Chen CH, Hong TP, Tseng VS, Lee CS (2009) A genetic-fuzzy mining approach for items with multiple minimum supports. Soft Comput 13(5):521–533CrossRefGoogle Scholar
  10. Cordón O, Herrera F, Villar P (2001) Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base. IEEE Trans Fuzzy Syst 9(4):667–674CrossRefGoogle Scholar
  11. Dunn JC (1973) “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters”. J Cybern 3:32–57MathSciNetzbMATHCrossRefGoogle Scholar
  12. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: The international conference on knowledge discovery and data mining, pp 226–231Google Scholar
  13. Fu A, Wong M, Sze S, Wong W, Wong W, Yu W (1998) Finding fuzzy sets for the mining of fuzzy association rules for numerical attributes. In: The international symposium on intelligent data engineering and learning, pp 263–268Google Scholar
  14. Gacto MJ, Alcalá R, Herrera F (2009) Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems. Soft Comput 13(3):419–436CrossRefGoogle Scholar
  15. 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. IEEE Trans Info Technol Biomed 10(1):28–41CrossRefGoogle Scholar
  16. Herrera F, Lozano M, Verdegay JL (1997) Fuzzy connectives based crossover operators to model genetic algorithms population diversity. Fuzzy Sets Syst 92(1):21–30CrossRefGoogle Scholar
  17. Hong TP, Lee YC (2001) Mining coverage-based fuzzy rules by evolutional computation. In: The IEEE international conference on data mining, pp 218–224Google Scholar
  18. Hong TP, Kuo CS, Chi SC (1999) A data mining algorithm for transaction data with quantitative values. In: The eighth international fuzzy systems association world congress, pp 874-878Google Scholar
  19. Hong TP, Kuo CS, Chi SC (2001) Trade-off between time complexity and number of rules for fuzzy mining from quantitative data. Int J Uncertain Fuzziness Knowl Based Syst 9(5):587–604zbMATHGoogle Scholar
  20. Hong TP, Chen CH, Wu YL, Lee YC (2006) A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions. Soft Comput 10(11):1091–1101CrossRefGoogle Scholar
  21. Hong TP, Chen CH, Lee YC, Wu YL (2008) Genetic-fuzzy data mining with divide-and-conquer strategy. IEEE Trans Evol Comput 12(2):252–265CrossRefGoogle Scholar
  22. Ishibuchi H, Yamamoto T (2005) Rule weight specification in fuzzy rule-based classification systems. IEEE Trans Fuzzy Syst 13(4):428–435CrossRefGoogle Scholar
  23. Kaya M, Alhajj R (2005) Genetic algorithm based framework for mining fuzzy association rules. Fuzzy Sets Syst 152(3):587–601MathSciNetzbMATHCrossRefGoogle Scholar
  24. Kuok C, Fu A, Wong M (1998) Mining fuzzy association rules in databases. SIGMOD Rec 27(1):41–46CrossRefGoogle Scholar
  25. Lee YC, Hong TP, Lin WY (2004) Mining fuzzy association rules with multiple minimum supports using maximum constraints. Lect Notes Comput Sci 3214:1283–1290CrossRefGoogle Scholar
  26. Liang H, Wu Z, Wu Q (2002) A fuzzy based supply chain management decision support system. World Congr Intell Control Autom 4:2617–2621Google Scholar
  27. Mangalampalli A, Pudi V (2009) Fuzzy association rule mining algorithm for fast and efficient performance on very large datasets. In: The IEEE international conference on fuzzy systems, pp 1163–1168Google Scholar
  28. McQueen JB (1967) Some methods of classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, pp 281–297Google Scholar
  29. Mohamadlou H, Ghodsi R, Razmi J, Keramati A (2009) A method for mining association rules in quantitative and fuzzy data. In: The international conference on computers & industrial engineering, pp 453–458Google Scholar
  30. Ouyang W, Huang Q (2009) Mining direct and indirect weighted fuzzy association rules in large transaction databases. Int Conf Fuzzy Syst Knowl Discov 3:128–132CrossRefGoogle Scholar
  31. Parodi A, Bonelli P (1993) A new approach of fuzzy classifier systems. In: The fifth international conference on genetic algorithms. Morgan Kaufmann, Los Altos, CA, pp 223–230Google Scholar
  32. Rasmani KA, Shen Q (2004) Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers. IEEE Int Conf Fuzzy Syst 3:1679–1684Google Scholar
  33. Roubos H, Setnes M (2001) Compact and transparent fuzzy models and classifiers through iterative complexity reduction. IEEE Trans Fuzzy Syst 9(4):516–524CrossRefGoogle Scholar
  34. Setnes M, Roubos H (2000) GA-fuzzy modeling and classification: complexity and performance. IEEE Trans Fuzzy Syst 8(5):509–522CrossRefGoogle Scholar
  35. Siler W, James J (2004) Fuzzy expert systems and fuzzy reasoning. Wiley, LondonCrossRefGoogle Scholar
  36. Wang CH, Hong TP, Tseng SS (1998) Integrating fuzzy knowledge by genetic algorithms. IEEE Trans Evol Comput 2(4):138–149CrossRefGoogle Scholar
  37. Wang CH, Hong TP, Tseng SS (2000) Integrating membership functions and fuzzy rule sets from multiple knowledge sources. Fuzzy Sets Syst 112:141–154CrossRefGoogle Scholar
  38. Yue S, Tsang E, Yeung D, Shi D (2000) Mining fuzzy association rules with weighted items. In: The IEEE international conference on systems, man and cybernetics, pp 1906–1911Google Scholar
  39. Zhang H, Liu D (2006) Fuzzy modeling and fuzzy control. Springer, BerlinzbMATHGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Chun-Hao Chen
    • 1
  • Tzung-Pei Hong
    • 2
    • 3
  • Vincent S. Tseng
    • 4
  1. 1.Department of Computer Science and Information EngineeringTamkang UniversityTaipeiTaiwan, ROC
  2. 2.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan, ROC
  3. 3.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan, ROC
  4. 4.Department of Computer Science and Information EngineeringNational Cheng-Kung UniversityTainanTaiwan, ROC

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