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Dynamic optimisation based fuzzy association rule mining method


Techniques of performance analysis, comprising of various metrics such as accuracy, efficiency and consuming time, have been conducted to evaluate the measures of properties and interestingness for the association rule mining method. Therefore, these metrics combined with different parameters (partitioning points, fuzzy sets) should be analysed thoroughly and balanced simultaneously to enhance the entire performance (effectiveness, accuracy and efficiency) for an algorithm. As a result, Most of the current algorithms face the pressure from the tradeoff of these metrics and parameters, which becomes even rougher when we employ it in different resources of data (discrete data, categorical data and continuous data). Specifically, serial data (i.e., sequences or transactions of floating point numbers), such as analysis of sensor streaming data, financial streaming data, medical streaming data and sentimental streaming data, are different from discrete variables, such as boolean data (e.g., sentiment: negative and positive represented as ‘0’ and ‘1’ separately) and categorical data (e.g., ‘young age’, ‘middle age’, ‘old age’). The main difference is that serial data face sharp boundary’s problem. That is, it is hard to decide the boundary values (i.e., the single points to partition data into different value groups), which is few to be solved in association rule mining methods. This paper aims to resolve the problem of sharp boundaries and balance multiple performances of our algorithm simultaneously by developing a novel dynamic optimisation (parameters and metrics) based fuzzy association rule mining (DOFARM) method. The proposed method can be applied in a wide range of classifying problems, such as the classification of sentiment strength (negative and positive). In our DOFARM method, instead of single partitioning points, we use a range of values to smoothly separate two consecutive partitions and develop a corresponding membership function to generate fuzzy sets for original data sets of physical and emotional diseases. Mainly, we design a dual compromise scheme: the first tradeoff balances better performance of out-putting association rules and more widely applicable fuzzy membership function while the second tradeoff reduces the time parameter as well as enhances the entire performance of our DOFARM method. The feasibility and accuracy of DOFARM we proposed have been certified theoretically and experimentally. Besides, we demonstrate the accuracy, effectiveness and efficiency for our DOFARM method by experiments according to both synthesis and real datasets.

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  1. 1.

    Zhou Q, Shi P, Xu S, Li H (2013) Adaptive output feedback control for nonlinear time-delay systems by fuzzy approximation approach. IEEE Trans Fuzzy Syst 21(2):301–313

    Article  Google Scholar 

  2. 2.

    Lee JH, Kwang HL (1997) An extension of association rules using fuzzy sets. In: Proceedings of the Seventh IFSA World Congress, pp 399-402

  3. 3.

    Delgado M, Marłn N, Snchez D (2003) Fuzzy association rules: general model and applications. IEEE Trans Fuzzy Syst 11(2):214–225

    Article  Google Scholar 

  4. 4.

    Ashfaq Wang XZ RAR, Fu AM (2015) Fuzziness based sample categorization for classifier performance improvement. J Intell Fuzzy Syst 29(3):1185–1196

    MathSciNet  Article  Google Scholar 

  5. 5.

    De Cock M, Cornelis C, Kerre EE (2003) Fuzzy association rules: a two-sided approach. In: Proceedings of Internal Conference on Fuzzy Information Processing-Theories and Applications, pp 385-390

  6. 6.

    Chen CL, Tseng FSC, Liang T (2010) An integration of WordNet and fuzzy association rule mining for multi-label document clustering. Data Knowl Eng 69:1208–1226

    Article  Google Scholar 

  7. 7.

    Alcalá-Fdez J, Alcalá R, Herrera F (2011) A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Trans Fuzzy Syst 19(5):857–872

    Article  Google Scholar 

  8. 8.

    Rao VV, Rambabu E, Sriramganesh G (2012) Effective association rule mining using Fuzzy Apriori and weighted Fuzzy Apriori. IJECCE 3(3):381–386

    Google Scholar 

  9. 9.

    Alcalá-Fdez J, Alcalá R, Gacto MJ, Herrera F (2009) Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets Syst 160(7):905–921

    MathSciNet  Article  MATH  Google Scholar 

  10. 10.

    Rao Y, Xie H, Li J, Jin F, Wang FL, Li Q (2016) Social emotion classification of short text via topic-level maximum entropy model. Inf Manag 53(8):978–986

    Article  Google Scholar 

  11. 11.

    Xie H, Li X, Wang T, Lau R, Wong TL, Chen L, Li Q (2016) Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy. Inf Process Manag 52(1):61–72

    Article  Google Scholar 

  12. 12.

    Dridi A, Recupero DR (2017) Leveraging semantics for sentiment polarity detection in social media. Int J Mach Learn Cybern 1:11

    Google Scholar 

  13. 13.

    Wang W, Tan G, Wang H (2017) Cross-domain comparison of algorithm performance in extracting aspect-based opinions from Chinese online reviews. Int J Mach Learn Cybern 8(3):1053–1070

    Article  Google Scholar 

  14. 14.

    Omar OB, Boschi-Pinto C, Lopez AD (2001) Age standardization of rates: a new WHO standard. World Health Organ 13(2):167–192

    Google Scholar 

  15. 15.

    Li X, Xie H, Chen L, Wang J, Deng X (2014) News impact on stock price return via sentiment analysis. Knowl Based Syst 69:14–23

    Article  Google Scholar 

  16. 16.

    Zheng H, He J, Huang GY, Zhang YC (2014) Optimised fuzzy association rule mining for quantitative data. In: Proceedings of 2014 IEEE International Conference on Fuzzy Systems, pp 396-403

  17. 17.

    Delgado M, SáNchez D, MartıN-Bautista MJ, Vila MA (2001) Mining association rules with improved semantics in medical databases. Artif Intell Med 21(1):241–245

    Article  Google Scholar 

  18. 18.

    Huang J, Peng M, Wang H, Cao J, Gao W, Zhang X (2017) A probabilistic method for emerging topic tracking in microblog stream. World Wide Web 20(2):325–350

    Article  Google Scholar 

  19. 19.

    Ma J, Sun L, Wang H, Zhang Y, Aickelin U (2016) Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Trans Internet Technol (TOIT) 16(1):1–4

    Article  Google Scholar 

  20. 20.

    Wang H, Zhang Y, Cao J (2009) Effective collaboration with information sharing in virtual universities. IEEE Trans Knowl Data Eng 21(6):840–853

    Article  Google Scholar 

  21. 21.

    Wang H, Cao J, Zhang Y (2005) A flexible payment scheme and its role-based access control. IEEE Trans Knowl Data Eng 17(3):425–436

    Article  Google Scholar 

  22. 22.

    Han J, Kamber M, Pei J (2006) Data mining: concepts and techniques, Morgan kaufmann, pp 229-242

  23. 23.

    Zhang J, Tao X, Wang” H (2014) Outlier detection from large distributed databases. World Wide Web 17(4):539–568

    Article  Google Scholar 

  24. 24.

    Kabir ME, Wang H, Bertino” E (2011) Efficient systematic clustering method for k-anonymization. Acta Inf 48(1):51–66

    MathSciNet  Article  MATH  Google Scholar 

  25. 25.

    Khalil F, Li J, Wang” H (2009) An integrated model for next page access prediction. Int J Knowl Web Intell 1(1):48–80

    Article  Google Scholar 

  26. 26.

    Sun X, Wang H, Li J, Pei” J (2011) Publishing anonymous survey rating data. Data Min Knowl Discov 23(3):379–406

    MathSciNet  Article  MATH  Google Scholar 

  27. 27.

    Wang XZ, Dong CR, Fan TG (2007) Training T-S norm neural networks to refine weights for fuzzy if-then rules. Neurocomputing 70(13–15):2581–2587.

    Article  Google Scholar 

  28. 28.

    Wang XZ, Hong JR (1998) On the handling of fuzziness for continuous-valued attributes in decision tree generation. Fuzzy Sets Syst 99(3):283–290.

    MathSciNet  Article  MATH  Google Scholar 

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This work is supported in part by the ARC Discovery Early Career Research Award (DE130100911), the ARC Discovery Project (DP130101327), the ARC Linkage Project (LP100200682), the NSFC funding (61332013), the International Science and Technology Cooperation Projects (No.2016D10008, 2013DFG12810, 2013C24027), the Municipal Natural Science Foundation of Ningbo (No.2015A610119), the Natural Science Foundation of of Zhejiang Province (No. Y16F020002) and the Guangzhou Science and Technology Project under Grants (2016201604030034).

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Correspondence to Jing He.

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Zheng, H., He, J., Huang, G. et al. Dynamic optimisation based fuzzy association rule mining method. Int. J. Mach. Learn. & Cyber. 10, 2187–2198 (2019).

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  • Association rule
  • Optimised parameters
  • Multiple objective function
  • Data mining