Abstract
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.
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References
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 73–80
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 487–499
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):221–235
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):121–144
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 61–73
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 1–10
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 1020–1024
Ha H, Hwang D, Ryu B, Yun KH (2003) Mining association rules on significant rare data using relative support. J Syst Softw 67(3):181–191
Herrera F, Carmona CJ, González P, del Jesus MJ (2011) An overview on subgroup discovery: foundations and applications. Knowl Inf Syst 29(3):495–525
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):365–396
Kim J, Kang J (2014) Towards identifying unresolved discussions in student online forums. Appl Intell 40(4):601–612
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) 3518:97–106
Koh YS, Rountree N (2010) Rare association rule mining and knowledge discovery: technologies for infrequent and critical event detection. Information Science Reference, Hershey
Lu J (2004) Personalized e-learning material recommender system. In: Proceedings of the international conference on information technology for application, pp 374–379
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 121–132
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):53–76
Luna JM, Romero JR, Ventura S (2014) On the adaptability of g3parm to the extraction of rare association rules. Knowl Inf Syst 38(2):391–418
Merceron A, Yacef K (2008) Interestingness measures for association rules in educational data. Educ Data Min
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):319–346
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 320–327
Ordoñez C, Ezquerra N, Santana C (2006) Constraining and summarizing association rules in medical data. Knowl Inf Syst 9(3):259–283
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 204–212
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 76–93
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 1–19
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, 171–180
Romero C, Luna JM, Romero JR, Ventura S (2011) Rm-tool: a framework for discovering and evaluating association rules. Adv Eng Softw 42(8):566–576
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):425–464
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):601–618
Romero C, Ventura S, García E (2008) Data mining in course management systems: Moodle case study and tutorial. Comput Educ 51(1):368–384
Romero C, Ventura S, Pechenizky M, Baker R (2010) Handbook of educational data mining. Chapman and Hall/CRC Press
Sánchez D, Serrano JM, Cerda L, Vila MA (2008) Association rules applied to credit card fraud detection. Expert Syst Appl 36:3630–3640
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 305–312
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 York
Yen S, Lee Y, Wang C (2014) An efficient algorithm for incrementally mining frequent closed itemsets. Appl Intell 40(4):649–668
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, Oslo
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, DC
Zhang C, Zhang S (2002) Association rule mining: models and algorithms. Springer, Berlin
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 827–831
Acknowledgments
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.
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Luna, J.M., Romero, C., Romero, J.R. et al. An evolutionary algorithm for the discovery of rare class association rules in learning management systems. Appl Intell 42, 501–513 (2015). https://doi.org/10.1007/s10489-014-0603-4
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DOI: https://doi.org/10.1007/s10489-014-0603-4