INCREMENTAL RELATIONAL ASSOCIATION RULE MINING OF EDUCATIONAL DATA SETS
DOI:
https://doi.org/10.24193/subbi.2018.2.07Keywords:
Data mining, Educational data mining, Relational association rule, Incremental algorithm.Abstract
Educational Data Mining is an attractive research field in which the underlying idea is that of bringing the data mining perspective into educational environments. The main focus is to better understand the educational related phenomena by extracting, through data mining techniques, meaningful hidden patterns from educational data sets. Incremental Relational Association Rule Mining (IRARM) has been introduced as an effective online data mining method for dynamically mining interesting relational association rules (RARs) in a dynamic data set which is extended with new data instances. The study conducted in this paper is aimed to emphasize the effectiveness of both RAR and IRARM mining methods in educational data mining settings. Experiments performed on various academic data sets highlight the potential of using relational association rules for uncovering relevant knowledge from educational related data.
References
Academic data set, 2018. http://www.cs.ubbcluj.ro/∼liana.crivei/AcademicDataSets.
Ahmed Mohamed Ahmed, Ahmet Rizaner, and Ali Hakan Ulusoy. Using data mining to predict instructor performance. Procedia Computer Science, 102:137 – 142, 2016. 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, 29-30 August 2016, Vienna, Austria.
Elizabeth Ayers, Rebecca Nugent, and Nema Dean. A comparison of student skill knowledge estimates. In Educational Data Mining - EDM 2009, Cordoba, Spain, July 1-3, 2009. Proceedings of the 2nd International Conference on Educational Data Mining., pages 1–10, 2009.
Brijesh Kumar Baradwaj and Saurabh Pal. Mining educational data to analyze students’ performance. CoRR, abs/1201.3417, 2012.
Alejandro Bogar´ın, Rebeca Cerezo, and Cristóbal Romero. A survey on educational process mining. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery, 8(1), 2018.
Alina Cˆampan, Gabriela Șerban, and Andrian Marcus. Relational association rules and error detection. Studia Universitatis Babes-Bolyai Informatica, LI(1):31–36, 2006.
Alina Campan, Gabriela Șerban, Traian Marius Truta, and Andrian Marcus. An algorithm for the discovery of arbitrary length ordinal association rules. In DMIN, pages 107–113, 2006.
Toshi Chandraker and Neelabh Sao. Incremental mining on association rules. International Jurnal of Engineering and Science, 1(11):31–33, 2012.
H. Y. Chang, J. C. Lin, M. L. Cheng, and S. C. Huang. A novel incremental data mining algorithm based on fp-growth for big data. In 2016 International Conference on Networking and Network Applications (NaNA), pages 375–378, July 2016.
I. G. Czibula, G. Czibula, D.-L. Miholca. Enhancing relational association rules with gradualness. International Journal of Innovative Computing, Information & Control, 13(1):289-305, 2017.
Gabriela Șerban, Alina Cˆampan, and Istvan Gergely Czibula. A programming interface for finding relational association rules. International Journal of Computers, Communications & Control, I(S.):439–444, June 2006.
Gabriela Czibula, Maria-Iuliana Bocicor, and Istvan Gergely Czibula. Promoter sequences prediction using relational association rule mining. Evolutionary Bioinformatics, 8:181–196, 04 2012.
M. Dhanabhakyam and M. Punithavalli. An efficient market basket analysis based on adaptive association rule mining with faster rule generation algorithm. The Standard International Journals on Computer Science Engineering and its Applications (CSEA), 1(3):105–110, 2013.
N. Gunduz and E. Fokoue. UCI machine learning repository, 2013.
Syed Tanveer Jishan, Raisul Islam Rashu, Naheena Haque, and Rashedur M. Rahman. Improving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique. Decision Analytics, 2(1):1, Mar 2015.
Yao Li, Zhi-Heng Zhang, Wen-Bin Chen, and Fan Min. Tdup: an approach to incremental mining of frequent itemsets with three-way-decision pattern updating. International Journal of Machine Learning and Cybernetics, 8(2):441–453, Apr 2017.
Diana-Lucia Miholca, Gabriela Czibula, and Liana Maria Crivei. A new incremental relational association rules mining approach. In 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES2018, page to be published. Procedia Computer Science, 2018.
Siti Khadijah Mohamad and Zaidatun Tasir. Educational data mining: A review. Procedia - Social and Behavioral Sciences, 97:320 – 324, 2013. The 9th International Conference on Cognitive Science.
B. Nath, D. K. Bhattacharyya, and A. Ghosh. Incremental association rule mining: A survey. Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(3):157–169, 2013.
Adewale O. Ogunde, Olusegun Folorunso, and Adesina S. Sodiya. The design of an adaptive incremental association rule mining system. In Proceedings of the World Congress on Engineering 2015 - Volume I, London, UK, 2015.
Kumar Ajay Pal and Saurabh Pal. Analysis and mining ofeducational data forpredictingthe performance of students. International Journal of Electronics Communication and Computer Engineering, 4(5):278—-4209, 2013.
N. L. Sarda and N. V. Srinivas. An adaptive algorithm for incremental mining of association rules. In Proceedings of the 9th International Workshop on Database and Expert Systems Applications, DEXA ’98, pages 240–, Washington, DC, USA, 1998. IEEE Computer Society.
Amirah Mohamed Shahiri, Wahidah Husain, and Nur’aini Abdul Rashid. A review on predicting student’s performance using data mining techniques. Procedia Computer Science, 72:414 – 422, 2015. The Third Information Systems International Conference 2015.
Thi-Oanh Tran, Hai-Trieu Dang, Viet-Thuong Dinh, Thi-Minh-Ngoc Truong, Thi-Phuong-Thao Vuong, and Xuan-Hieu Phan. Performance prediction for students: A multi-strategy approach. CYBERNETICS AND INFORMATION TECHNOLOGIES, 17(2):164 – 182, 2017.
Eiad Yafi, Ahmed Al-Hegami, Afshar Alam, and Ranjit Biswas. YAMI: Incremental mining of interesting association patterns. The International Arab Jurnal of Information Technology, 9(6):504–510, 2012.
Guo Yu-Dong, Li Sheng-Lin, Li Yong-Zhi, Wang Zhao-Xia, and Zeng Li. Large-scale dataset incremental association rules mining model and optimization algorithm. International Journal of Database Theory and Application, 9(4):195–208, 2016.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 Studia Universitatis Babeș-Bolyai Informatica
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
