Applied Intelligence

, Volume 42, Issue 3, pp 501–513 | Cite as

An evolutionary algorithm for the discovery of rare class association rules in learning management systems

  • J. M. Luna
  • C. Romero
  • J. R. Romero
  • S. VenturaEmail author


Association rule mining, an important data mining technique, has been widely focused on the extraction of frequent patterns. Nevertheless, in some application domains it is interesting to discover patterns that do not frequently occur, even when they are strongly related. More specifically, this type of relation can be very appropriate in e-learning domains due to its intrinsic imbalanced nature. In these domains, the aim is to discover a small but interesting and useful set of rules that could barely be extracted by traditional algorithms founded in exhaustive search-based techniques. In this paper, we propose an evolutionary algorithm for mining rare class association rules when gathering student usage data from a Moodle system. We analyse how the use of different parameters of the algorithm determine the rule characteristics, and provides some illustrative examples of them to show their interpretability and usefulness in e-learning environments. We also compare our approach to other existing algorithms for mining both rare and frequent association rules. Finally, an analysis of the rules mined is presented, which allows information about students’ unusual behaviour regarding the achievement of bad or good marks to be discovered.


Rare association rules Grammar guided genetic programming Evolutionary computation Educational data mining 



This research was supported by the Regional Government of Andalusia, project P08-TIC-3720, by the Spanish Ministry of Science and Technology, project TIN-2011-22408, and by FEDER funds. This research was also supported by the Spanish Ministry of Education under FPU grant AP2010-0041.


  1. 1.
    Adda M, Wu L, Feng Y (2007) Rare itemset mining. In: Proceedings of the 6th international conference on machine learning and applications. ICMLA ’07. Cincinnati, pp 73–80Google Scholar
  2. 2.
    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases. VLDB ’94. Morgan Kaufmann Publishers Inc, San Francisco, pp 487–499Google Scholar
  3. 3.
    Berzal F, Blanco I, Sánchez D, Vila MA (2002) Measuring the accuracy and interest of association rules: a new framework. Intell Data Anal 6(3):221–235zbMATHGoogle Scholar
  4. 4.
    Espejo PG, Ventura S, Herrera F (2010) A survey on the application of genetic programming to classification. IEEE Trans Syst Man Cybern: Part C 40(2):121–144CrossRefGoogle Scholar
  5. 5.
    Fournier-Viger P, Wu C, Tseng VS (2012) Mining top-k association rules. In: Proceedings of the 25th Canadian conference on advances in artificial intelligence. Canadian AI’12, pp 61–73Google Scholar
  6. 6.
    Freyberger J, Heffernan NT, Ruiz C (2004) Using association rules to guide a search for best fitting transfer models of student learning. In: Workshop on analyzing student-tutor interaction logs to improve educational outcomes. ICITS 2004, pp 1–10Google Scholar
  7. 7.
    Gu Q, Cai Z, Zhu L, Huang B (2008) Data mining on imbalanced data sets. In: Proceedings of the international conference on advanced computer theory and engineering, pp 1020–1024Google Scholar
  8. 8.
    Ha H, Hwang D, Ryu B, Yun KH (2003) Mining association rules on significant rare data using relative support. J Syst Softw 67(3):181–191CrossRefGoogle Scholar
  9. 9.
    Herrera F, Carmona CJ, González P, del Jesus MJ (2011) An overview on subgroup discovery: foundations and applications. Knowl Inf Syst 29(3):495–525CrossRefGoogle Scholar
  10. 10.
    Hoai RI, Whigham NX, Shan PA, O’neill Y, McKay M (2010) Grammar-based genetic programming: a survey. Genet Program Evolvable Mach 11(3–4):365–396Google Scholar
  11. 11.
    Kim J, Kang J (2014) Towards identifying unresolved discussions in student online forums. Appl Intell 40(4):601–612CrossRefGoogle Scholar
  12. 12.
    Koh YS, Rountree N (2005) Finding sporadic rules using apriori-inverse. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3518:97–106Google Scholar
  13. 13.
    Koh YS, Rountree N (2010) Rare association rule mining and knowledge discovery: technologies for infrequent and critical event detection. Information Science Reference, HersheyCrossRefGoogle Scholar
  14. 14.
    Lu J (2004) Personalized e-learning material recommender system. In: Proceedings of the international conference on information technology for application, pp 374–379Google Scholar
  15. 15.
    Luna JM, Romero JR, Romero C, Ventura S (2013) Discovering subgroups by means of genetic programming. In: Proceedings of the 16th European conference. EuroGP 2013. Vienna, pp 121–132Google Scholar
  16. 16.
    Luna JM, Romero JR, Ventura S (2012) Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules. Knowl Inf Syst 32(1):53–76CrossRefGoogle Scholar
  17. 17.
    Luna JM, Romero JR, Ventura S (2014) On the adaptability of g3parm to the extraction of rare association rules. Knowl Inf Syst 38(2):391–418CrossRefGoogle Scholar
  18. 18.
    Merceron A, Yacef K (2008) Interestingness measures for association rules in educational data. Educ Data MinGoogle Scholar
  19. 19.
    Merceron A, Yacef K (2004) Mining student data captured from a web-based tutoring tool: initial exploration and results. J Interact Learn Res 15(4):319–346Google Scholar
  20. 20.
    Minaei-Bidgoli B, Tan PN, Punch WF (2004) Mining interesting contrast rules for a web-based educational system. In: Proceedings of the international conference on machine learning applications. ICMLA. IEEE Computer Society, pp 320–327Google Scholar
  21. 21.
    Ordoñez C, Ezquerra N, Santana C (2006) Constraining and summarizing association rules in medical data. Knowl Inf Syst 9(3):259–283CrossRefGoogle Scholar
  22. 22.
    Raedt L, Guns T, Nijssen S (2008) Constraint programming for data mining and machine learning. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. SIGKDD, pp 204–212Google Scholar
  23. 23.
    Rahman A, Ezeife CI, Aggarwal AK (2008) Wifi miner: an online apriori-infrequent based wireless intrusion system. In: Proceedings of the 2nd international workshop in knowledge discovery from sensor data. Sensor-KDD ’08. Las Vegas, pp 76–93Google Scholar
  24. 24.
    Ramli AA (2005) Web usage mining using apriori algorithm: Uum learning care portal case. In: Proceedings of the international conference on knowledge management. Malaysia, pp 1–19Google Scholar
  25. 25.
    Romero C, Luna JM, Romero JR, Ventura S (2010) Mining rare association rules from e-learning data. In: Proceedings of the 3rd international conference on educational data mining. EDM 2010, 171–180Google Scholar
  26. 26.
    Romero C, Luna JM, Romero JR, Ventura S (2011) Rm-tool: a framework for discovering and evaluating association rules. Adv Eng Softw 42(8):566–576CrossRefGoogle Scholar
  27. 27.
    Romero C, Ventura C, De Bra P (2004) Knowledge discovery with genetic programming for providing feedback to courseware author. User modeling and user-adapted interaction. J Personal Res 5(14):425–464Google Scholar
  28. 28.
    Romero C, Ventura S (2010) Educational data mining: a review of the state of the art. IEEE Trans Syst Man Cybern Part C 40(6):601–618CrossRefGoogle Scholar
  29. 29.
    Romero C, Ventura S, García E (2008) Data mining in course management systems: Moodle case study and tutorial. Comput Educ 51(1):368–384CrossRefGoogle Scholar
  30. 30.
    Romero C, Ventura S, Pechenizky M, Baker R (2010) Handbook of educational data mining. Chapman and Hall/CRC PressGoogle Scholar
  31. 31.
    Sánchez D, Serrano JM, Cerda L, Vila MA (2008) Association rules applied to credit card fraud detection. Expert Syst Appl 36:3630–3640CrossRefGoogle Scholar
  32. 32.
    Szathmary L, Napoli A, Valtchev P (2007) Towards rare itemset mining. In: Proceedings of the 19th IEEE international conference on tools with artificial intelligence. ICTAI ’07. Patras, pp 305–312Google Scholar
  33. 33.
    Tan P, Kumar V (2000) Interestingness measures for association patterns: a perspective. In: Proceedings of the workshop on postprocessing in machine learning and data mining. KDD ’00. New YorkGoogle Scholar
  34. 34.
    Yen S, Lee Y, Wang C (2014) An efficient algorithm for incrementally mining frequent closed itemsets. Appl Intell 40(4):649–668CrossRefGoogle Scholar
  35. 35.
    Yu P, Own C, Lin L (2001) On learning behavior analysis of web based interactive environment. In: Proceedings of the international conference on implementing curricular change in engineering education. ICCEE, OsloGoogle Scholar
  36. 36.
    Zaíane OR (2002) Building a recommender agent for e-learning systems. In: Proceedings of the international conference on computers in education. ICCE ’02. IEEE Computer Society, Washington, DCGoogle Scholar
  37. 37.
    Zhang C, Zhang S (2002) Association rule mining: models and algorithms. Springer, BerlinCrossRefGoogle Scholar
  38. 38.
    Zhang H, Zhao Y, Cao L, Zhang C (2007) Class association rule mining with multiple imbalanced attributes. In: Orgun MA, Thornton J (eds) AI 2007: advances in artificial intelligence, volume 4830 of Lecture Notes in Computer Science. Springer, Berlin, pp 827–831Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • J. M. Luna
    • 1
  • C. Romero
    • 1
  • J. R. Romero
    • 1
  • S. Ventura
    • 1
    • 2
    Email author
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CordobaCordobaSpain
  2. 2.Department of Computer ScienceKing Abdulaziz UniversityJeddahSaudi Arabia Kingdom

Personalised recommendations