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A Systematic Assessment of Numerical Association Rule Mining Methods

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Abstract

In data mining, the classical association rule mining techniques deal with binary attributes; however, real-world data have a variety of attributes (numerical, categorical, Boolean). To deal with the variety of data attributes, the classical association rule mining technique was extended to numerical association rule mining. Initially, the concept of numerical association rule mining started with the discretization method, and later, many other methods, e.g., optimization, distribution are proposed in state-of-the-art. Different authors have presented various algorithms for each numerical association rule mining method; therefore, it is hard to select a suitable algorithm for a numerical association rule mining task. In this article, we present a systematic assessment of various numerical association rule mining methods and we provide a meta-study of thirty numerical association rule mining algorithms. We investigate how far the discretization techniques have been used in the numerical association rule mining methods.

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References

  1. Agbehadji IE, Fong S, Millham R. Wolf Search Algorithm for numeric association rule mining. In: IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), IEEE; 2016. pp. 146–51.

  2. Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. ACM SIGMOD Record. 1993;22(2):207–16. https://doi.org/10.1145/170036.170072.

    Article  Google Scholar 

  3. Agrawal R, Srikant R. Fast Algorithms for mining association rules in large databases. In: Proceedings of 20th International Conference on Very Large Data Bases, Morgan Kaufmann;1994 p. 487–99.

  4. Alatas B, Akin E. Rough particle swarm optimization and its applications in data mining. Soft Comput. 2008;12(12):1205–18.

    MATH  Google Scholar 

  5. Alatas B, Akin E. Chaotically encoded particle swarm optimization algorithm and its applications. Chaos Solit Fract. 2009;41(2):939–50.

    Google Scholar 

  6. Alatas B, Akin E. Multi-objective rule mining using a chaotic particle swarm optimization algorithm. Knowl Based Syst. 2009;22(6):455–60.

    Google Scholar 

  7. Alatas B, Akin E, Karci A. Modenar: multi-objective differential evolution algorithm for mining numeric association rules. Appl Soft Comput. 2008;8(1):646–56.

    Google Scholar 

  8. Altay EV, Alatas B. Performance analysis of multi-objective artificial intelligence optimization algorithms in numerical association rule mining. J Ambient Intell Human Comput. 2019;2019:1–21.

    Google Scholar 

  9. Altay EV, Alatas B. Intelligent optimization algorithms for the problem of mining numerical association rules. Phys A. 2020;540:123142.

    Google Scholar 

  10. Álvarez VP, Vázquez JM. An evolutionary algorithm to discover quantitative association rules from huge databases without the need for an a priori discretization. Expert Syst Appl. 2012;39(1):585–93.

    Google Scholar 

  11. Aumann Y, Lindell Y. A statistical theory for quantitative association rules. J Intell Inf Syst. 2003;20(3):255–83.

    Google Scholar 

  12. Beiranvand V, Mobasher-Kashani M, Bakar AA. Multi-objective pso algorithm for mining numerical association rules without a priori discretization. Expert Syst Appl. 2014;41(9):4259–73.

    Google Scholar 

  13. Can U, Alatas B. Automatic mining of quantitative association rules with gravitational search algorithm. Int J Softw Eng Knowl Eng. 2017;27(03):343–72.

    Google Scholar 

  14. Chan KC, Au WH. An effective algorithm for mining interesting quantitative association rules. In: Proceedings of the 1997 ACM symposium on Applied computing; 1997. pp. 88–90.

  15. Cui Y, Geng Z, Zhu Q, Han Y. Multi-objective optimization methods and application in energy saving. Energy. 2017;125:681–704.

    Google Scholar 

  16. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput. 2002;6(2):182–97.

    Google Scholar 

  17. Djenouri Y, Bendjoudi A, Djenouri D, Comuzzi, M. Gpu-based bio-inspired model for solving association rules mining problem. In: 2017 25th euromicro international conference on parallel, distributed and network-based processing (PDP), IEEE; 2017. pp. 262–9.

  18. Draheim D. Generalized Jeffrey conditionalization: a frequentist semantics of partial conditionalization. Berlin: Springer; 2017.

    MATH  Google Scholar 

  19. Eshelman LJ. The chc adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Foundations of genetic algorithms, Elsevier; 1991. vol. 1. pp. 265–83.

  20. Fister I, Iglesias A, Galvez A, Del Ser J, Osaba E. Differential evolution for association rule mining using categorical and numerical attributes. In: International conference on intelligent data engineering and automated learning, Springer; 2018. pp. 79–88.

  21. Fonseca CM, Fleming PJ et al. Genetic algorithms for multiobjective optimization: Formulation discussion and generalization. In: Icga, Citeseer; 1993. vol. 93, pp. 416–23.

  22. Fukuda T, Morimoto Y, Morishita S, Tokuyama T. Mining optimized association rules for numeric attributes. J Comput Syst Sci. 1999;58(1):1–12.

    MathSciNet  MATH  Google Scholar 

  23. Ghosh A, Nath B. Multi-objective rule mining using genetic algorithms. Inf Sci. 2004;163(1–3):123–33.

    MathSciNet  Google Scholar 

  24. Grabmeier J, Rudolph A. Techniques of cluster algorithms in data mining. Data Min Knowl Disc. 2002;6(4):303–60.

    MathSciNet  Google Scholar 

  25. Guo Y, Yang J, Huang Y. An effective algorithm for mining quantitative association rules based on high dimension cluster. In: 2008 4th international conference on wireless communications, networking and mobile computing, IEEE; 2008. pp. 1–4.

  26. Gyenesei A. A fuzzy approach for mining quantitative association rules. Acta Cybern. 2001;15(2):305–20.

    MathSciNet  MATH  Google Scholar 

  27. Han J, Pei J, Kamber M. Data mining: concepts and techniques. Hoboken: Elsevier; 2011.

    MATH  Google Scholar 

  28. Han J, Pei J, Yin Y, Mao R. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Disc. 2004;8(1):53–87.

    MathSciNet  Google Scholar 

  29. Hirasawa K, Okubo M, Katagiri H, Hu J, Murata J. Comparison between genetic network programming (gnp) and genetic programming (gp). In: Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546), IEEE; 2001. vol. 2, pp. 1276–82.

  30. Holland JH. Adaption in natural and artificial systems. In: An introductory analysis with application to biology, control and artificial intelligence; 1975.

  31. Hong TP, Kuo CS, Chi SC. Mining association rules from quantitative data. Intell Data Anal. 1999;3(5):363–76.

    MATH  Google Scholar 

  32. Kaushik M, Sharma R, Peious SA, Shahin M, Yahia SB, Draheim D. On the potential of numerical association rule mining. In: International conference on future data and security engineering, Springer; 2020. pp. 3–20.

  33. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, IEEE; 1995. vol. 4, pp. 1942–48.

  34. Khade R, Patel N, Lin J. Supervised dynamic and adaptive discretization for rule mining. In: 2015 In SDM Workshop on Big Data and Stream Analytics; 2015.

  35. Kianmehr K, Alshalalfa M, Alhajj R. Fuzzy clustering-based discretization for gene expression classification. Knowl Inf Syst. 2010;24(3):441–65.

    Google Scholar 

  36. Kim H, Adeli H. Discrete cost optimization of composite floors using a floating-point genetic algorithm. Eng Optim. 2001;33(4):485–501.

    Google Scholar 

  37. Koza JR, Koza JR. Genetic programming: on the programming of computers by means of natural selection, vol. 1. Berlin: MIT press; 1992.

    MATH  Google Scholar 

  38. Kuo R, Gosumolo M, Zulvia FE. Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining. Neural Comput Appl. 2019;31(8):3559–72.

    Google Scholar 

  39. Kwaśnicka H, Świtalski K. Discovery of association rules from medical data-classical and evolutionary approaches. Ann Univ Mariae Curie-Sklodowska Sect AI-Inf. 2006;4(1):204–17.

    Google Scholar 

  40. Lent B, Swami A, Widom J. Clustering association rules. In: Proceedings 13th international conference on data engineering, IEEE; 1997. pp. 220–31.

  41. Lian W, Cheung DW, Yiu S. An efficient algorithm for finding dense regions for mining quantitative association rules. Comput Math Appl. 2005;50(3–4):471–90.

    MathSciNet  MATH  Google Scholar 

  42. Liu H, Abraham A, Li Y, Yang X. Role of chaos in swarm intelligence a preliminary analysis. In: Applications of soft computing, Springer; 2006. pp. 383–92.

  43. Liu H, Hussain F, Tan CL, Dash M. Discretization: an enabling technique. Data Min Knowl Disc. 2002;6(4):393–423.

    MathSciNet  Google Scholar 

  44. Lud MC, Widmer G. Relative unsupervised discretization for association rule mining. In: Zighed DA, Komorowski J, Żytkow J, editors. Principles of data mining and knowledge discovery. Berlin, Heidelberg: Springer; 2000. p. 148–58.

    Google Scholar 

  45. Martín D, Rosete A, Alcalá-Fdez J, Herrera F. A multi-objective evolutionary algorithm for mining quantitative association rules. In: 2011 11th international conference on intelligent systems design and applications, IEEE; 2011. pp. 1397–402.

  46. Martínez-Ballesteros M, Troncoso A, Martínez-Álvarez F, Riquelme JC. Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integr Comput-Aided Eng. 2010;17(3):227–42.

    Google Scholar 

  47. Mata J, Alvarez J, Riquelme J. Mining numeric association rules with genetic algorithms. In: Artificial neural nets and genetic algorithms, Springer; 2001. pp. 264–7.

  48. Mata J, Alvarez JL, Riquelme JC. Discovering numeric association rules via evolutionary algorithm. In: Pacific-Asia conference on knowledge discovery and data mining, Springer; 2002. pp. 40–51.

  49. Mlakar U, Zorman M, Fister I Jr, Fister I. Modified binary cuckoo search for association rule mining. J Intell Fuzzy Syst. 2017;32(6):4319–30.

    Google Scholar 

  50. Moreland K, Truemper K. Discretization of target attributes for subgroup discovery. In: International workshop on machine learning and data mining in pattern recognition, Springer; 2009. pp. 44–52.

  51. Peious SA, Sharma R, Kaushik M, Shah SA, Yahia SB. Grand reports: a tool for generalizing association rule mining to numeric target values. In: International conference on big data analytics and knowledge discovery, Springer; 2020. pp. 28–37.

  52. Poli R, Kennedy J, Blackwell T. Particle swarm optimization. Swarm Intell. 2007;1(1):33–57.

    Google Scholar 

  53. Qodmanan HR, Nasiri M, Minaei-Bidgoli B. Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. Expert Syst Appl. 2011;38(1):288–98.

    Google Scholar 

  54. Rashedi E, Nezamabadi-Pour H, Saryazdi S. Gsa: a gravitational search algorithm. Inf Sci. 2009;179(13):2232–48.

    MATH  Google Scholar 

  55. Rashedi E, Rashedi E, Nezamabadi-pour H. A comprehensive survey on gravitational search algorithm. Swarm Evol Comput. 2018;41:141–58.

    MATH  Google Scholar 

  56. Salleb-Aouissi A, Vrain C, Nortet C, Kong X, Rathod V, Cassard D. Quantminer for mining quantitative association rules. J Mach Learn Res. 2013;14(1):3153–7.

    MATH  Google Scholar 

  57. Seki H, Nagao M. An efficient java implementation of a ga-based miner for relational association rules with numerical attributes. In: 2017 ieee international conference on systems, man, and cybernetics (SMC), IEEE; 2017. pp. 2028–33.

  58. Sharma R, Kaushik M, Peious SA, Yahia SB, Draheim D. Expected vs. unexpected: Selecting right measures of interestingness. In: International conference on big data analytics and knowledge discovery, Springer; 2020. pp. 38–47.

  59. Shih MY, Jheng JW, Lai LF. A two-step method for clustering mixed categroical and numeric data. Tamkang J Sci Eng. 2010;13(1):11–9.

    Google Scholar 

  60. Srikant R, Agrawal R. Mining quantitative association rules in large relational tables. In: Proceedings of the 1996 ACM SIGMOD international conference on Management of data; 1996. pp. 1–12.

  61. Srinivas N, Deb K. Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput. 1994;2(3):221–48.

    Google Scholar 

  62. Storn R, Price K. Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. J Glob Optim. 1995;1995:23.

    Google Scholar 

  63. Storn R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim. 1997;11(4):341–59.

    MathSciNet  MATH  Google Scholar 

  64. Taboada K, Gonzales E, Shimada K, Mabu S, Hirasawa K, Hu J. Association rule mining for continuous attributes using genetic network programming. IEEE J Trans Electr Electron Eng. 2008;3(2):199–211.

    Google Scholar 

  65. Tahyudin I, Nambo H. The combination of evolutionary algorithm method for numerical association rule mining optimization. In: Proceedings of the tenth international conference on management science and engineering management, Springer; 2017. pp. 13–23.

  66. Tan SC. Improving association rule mining using clustering-based discretization of numerical data. In: 2018 international conference on intelligent and innovative computing applications (ICONIC), IEEE; 2018. pp. 1–5.

  67. Tang R, Fong S, Yang XS, Deb S. Wolf search algorithm with ephemeral memory. In: Seventh international conference on digital information management (ICDIM 2012), IEEE; 2012. pp. 165–72.

  68. Telikani A, Gandomi AH, Shahbahrami A. A survey of evolutionary computation for association rule mining. Inf Sci. 2020;2020:5.

    MathSciNet  MATH  Google Scholar 

  69. Triguero I, García S, Herrera F. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recogn. 2011;44(4):901–16.

    Google Scholar 

  70. Webb GI. OPUS: An efficient admissible algorithm for unordered search. J Artif Intell Res. 1995;3:431–65. https://doi.org/10.1613/jair.227

  71. Webb GI. Discovering associations with numeric variables. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2001; pp. 383–8.

  72. Yamany W, Emary E, Hassanien AE. Wolf search algorithm for attribute reduction in classification. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM), IEEE. 2014; pp. 351–8. https://doi.org/10.1109/CIDM.2014.7008689

  73. Yan D, Zhao X, Lin R, Bai D. Ppqar Parallel pso for quantitative association rule mining. Peer-to-Peer Netw Appl. 2019;12(5):1433–44.

    Google Scholar 

  74. Yan X, Zhang C, Zhang S. Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Syst Appl. 2009;36(2):3066–76.

  75. Yang, J., Feng, Z. An effective algorithm for mining quantitative associations based on subspace clustering. In: International Conference on Networking and Digital Society IEEE; 2010;1:175–8.

  76. Zhang W. Mining fuzzy quantitative association rules. In: Proceedings of 11th International Conference on Tools with Artificial Intelligence, IEEE;1999. pp. 99–102.

  77. H. Zheng, J. He, G. Huang and Y. Zhang. Optimized fuzzy association rule mining for quantitative data. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE; 2014. pp. 396-403. https://doi.org/10.1109/FUZZ-IEEE.2014.6891735

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Authors and Affiliations

  1. Information Systems Group, Tallinn University of Technology, Akadeemia tee 15a, 12618, Tallinn, Estonia

    Minakshi Kaushik, Rahul Sharma, Sijo Arakkal Peious , Mahtab Shahin & Dirk Draheim

  2. Software Science Department, Tallinn University of Technology, Akadeemia tee 15a, 12618, Tallinn, Estonia

    Sadok Ben Yahia

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This article is part of the topical collection “Future Data and Security Engineering 2020” guest edited by Tran Khanh Dang.

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Kaushik, M., Sharma, R., Peious , S.A. et al. A Systematic Assessment of Numerical Association Rule Mining Methods. SN COMPUT. SCI. 2, 348 (2021). https://doi.org/10.1007/s42979-021-00725-2

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