Abstract
Finding the interesting rules from data repository is quite challenging weather for public or private sectors practitioners. Therefore, the purpose of this study is to apply an enhanced association rules mining method, so called SLP-Growth (Significant Least Pattern Growth) proposed by [11,36] to mining the interesting association rules based on the student admission dataset. The dataset contains the records of preferred programs being selected by post-matriculation or post-STPM students of Malaysia via Electronic Management of Admission System (e-MAS) for the year 2008/2009. The results of this study will provide useful information for educators and higher university authority personnel in the university to understand the programs’ patterns being selected by them.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Yukselture, E.,and Inan, F.A.: Examining the Factors Affecting Student Dropout in an Online Certificate Program. Turkish Online Journal of Distance Education, 7(3), July 2006, 76-88 (2006)
Mohammad, S., and El-Masri, A.Z.: Factors Affecting Dropouts Students in Arab Open University - Bahrain Branch. International Journal of Science and Technology, 2(7), July 2012, 435-442 (2012)
Stearns, E., and Glennie, E.J.: When and Why Dropouts Leave High School. Youth Society. SAGE Journal, vol. 38, no. 1, September 2006, 29-57 (2006)
Romero, C., and Ventura, S.: Educational Data Mining: A Survey from 1995 to 2005. Expert Systems with Applications, 33, 135–146 (2007)
Ceglar, A., Roddick, J.F.: Association Mining. ACM Computing Surveys, 38(2), 1–42 (2006)
Agrawal, R., Imielinski, T., and Swami, A.: Database Mining: A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering, 5 (6), 914-925 (1993)
Agrawal, R., Imielinski, T., and Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In Proceedings of the ACM SIGMOD ‘93 Intl. Conf. on the Management of Data, pp. 207–216 (1993)
Agrawal, R., and Srikant, R.: Fast Algorithms for Mining Association Rules. In Proceedings of the 20th Intl. Conf. on Very Large Data Bases (VLDB) 1994, pp.487–499 (1994)
Yun, H., Ha, D., Hwang, B., and Ryu, K.H.: Mining Association Rules on Significant Rare Data Using Relative Support. The Journal of Systems and Software, 67 (3), 181-19 (2003)
Xiong, H., Shekhar, S., Tan, P.N., and Kumar, V.: Exploiting A Support-Based Upper Bond Pearson’s Correlation Coefficient for Efficiently Identifying Strongly Correlated Pairs. In The Proceeding of ACM SIGKDD 2004, pp. 334-343 (2004)
Abdullah, Z., Herawan, T., and Deris, M.M.: Mining Significant Least Association Rules using Fast SLP-Growth Algorithm. In T.H. Kim and H. Adeli (Eds.): AST/UCMA/ISA/ACN 2010, LNCS, 6059, pp. 324–336. Springer Heidelberg (2010)
Abdullah, Z., Herawan, T., and Deris, M.M.: Scalable Model for Mining Critical Least Association Rules. In Rongbo Zhu et al. ICICA 2010, LNCS, 6377, pp. 509-516. Springer Heidelberg (2010)
Abdullah, Z., Herawan, T., Noraziah, A., and Deris, M.M.: Extracting Highly Positive Association Rules from Students’ Enrollment Data. Procedia Social and Behavioral Sciences, 28, 107–111 (2011)
Abdullah, Z., Herawan, T., Noraziah, A., and Deris, M.M.: Mining Significant Association Rules from Educational Data using Critical Relative Support Approach. Procedia Social and Behavioral Sciences, 28, 97–101 (2011)
Abdullah, Z., Herawan, T., and Deris, M.M.: An Alternative Measure for Mining Weighted Least Association Rule and Its Framework. In J.M. Zain et al. (Eds.): ICSECS 2011, CCIS, vol. 188, II, pp. 475–485. Springer Heidelberg (2011)
Abdullah, Z., Herawan, T., and Deris, M.M.: Visualizing the Construction of Incremental Disorder Trie Itemset Data Structure (DOSTrieIT) for Frequent Pattern Tree (FP-Tree). In H.B. Zaman et al. (Eds.): IVIC 2011, LNCS, 7066, pp. 183–195. Springer Heidelberg (2011)
Herawan, T., Vitasari, P., and Abdullah, Z.: Mining Interesting Association Rules of Student Suffering Mathematics Anxiety. In J.M. Zain et al. (Eds.): ICSECS 2011, CCIS, 188, II, pp. 495–508. Springer Heidelberg (2011)
Abdullah, Z., Herawan, T., and Deris, M.M.: Efficient and Scalable Model for Mining Critical Least Association Rules. In a special issue from AST/UCMA/ISA/ACN 2010, Journal of The Chinese Institute of Engineer, Taylor and Francis, 35, No. 4, 27 June 2012, 547–554 (2012)
Herawan, T., Abdullah, Z., Noraziah, A., Deris, M.M., and Abawajy, J.H.: IPMA: Indirect Patterns Mining Algorithm. In N.T. Nguyen et al. (Eds.): ICCCI 2012, AMCCISCI, vol. 457, pp. 187–196. Springer Heidelberg (2012).
Herawan, T., Abdullah, Z., Noraziah, A., Deris, M.M., and Abawajy, J.H.: EFP-M2: Efficient Model for Mining Frequent Patterns in Transactional Database. In N.T. Nguyen et al. (Eds.): ICCCI 2012, LNCS, pp. 7654, 29–38. Springer Heidelberg (2012).
N. Ahmad, Z. Abdullah, T. Herawan, and M.M. Deris. Scalable Technique to Discover Items Support from Trie Data Structure. In B. Liu et al. (Eds.): ICICA 2012, LNCS, vol. 7473, pp. 500–507. Springer Heidelberg (2012)
N. Ahmad, Z. Abdullah, T. Herawan, and M.M. Deris.: WLAR-Viz: Weighted Least Association Rules Visualization. In B. Liu et al. (Eds.): ICICA 2012, LNCS, 7473, pp. 592–600. Springer Heidelberg (2012)
Herawan, T., and Abdullah, Z.: CNAR-M: A Model for Mining Critical Negative Association Rules. In Zhihua Cai et al. (Eds): ISICA 2012, CCIS, 316, pp. 170–179. Springer Heidelberg (2012)
Abdullah, Z., Herawan, T., Ahmad, N., and Deris, M.M.: DFP-Growth: An Efficient Algorithm for Mining Pattern in Dynamic Database. In B. Liu et al. (Eds.): ICICA 2012, LNCS, vol. 7473, pp. 51–59. Springer Heidelberg (2012)
Abdullah, Z., Herawan, T., and Deris, M.M.: Detecting Critical Least Association Rules in Medical Databasess. International Journal of Modern Physics: Conference Series, World Scientific, 9, 464–479 (2012)
Herawan, T., Abdullah, Z., Noraziah, A., Deris, M.M., and Abawajy, J.H.: EFP-M2: Efficient Model for Mining Frequent Patterns in Transactional Database. In N.T. Nguyen et al. (Eds.): ICCCI 2012, LNCS, 7654, pp. 29–38. Springer Heidelberg (2012)
Zhou, L., and Yau, S.: Association Rule and Quantitative Association Rule Mining Among Infrequent Items. Rare Association Rule Mining and Knowledge Discovery, pp.15-32. IGI-Global (2010)
J. Ding.: Efficient Association Rule Mining among Infrequent Items. Ph.D Thesis, University of Illinois at Chicago. (2005)
Y.S. Koh and N. Rountree.: Finding Sporadic Rules using Apriori-Inverse. LNCS, vol. 3518, pp. 97-106. Springer Heidelberg (2005)
Liu, B., Hsu, W., and Ma, Y.: Mining Association Rules with Multiple Minimum Supports, SIGKDD Explorations, pp. 337 – 341 (1999)
Brin, S., Motwani, R., and Silverstein, C.: Beyond Market Baskets: Generalizing ARs to Correlations. Special Interest Group on Management of Data (SIGMOD’97), pp. 265-276 (1997)
Omniecinski, E.: Alternative Interest Measures for Mining Associations. IEEE Trans. Knowledge and Data Engineering, 15, 57-69 (2003)
Y.-K. Lee, W.-Y. Kim, Y.D. Cai, J. Han. CoMine: Efficient Mining of Correlated Patterns. The Proceeding of ICDM’03 (2003)
Han, J., Pei, J., Yin, Y., and Mao, R.: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach∗. Data Mining and Knowledge Discovery, 8, 53–87 (2004)
Han, J., and Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd ed., (2006)
Abdullah, Z., Herawan, T., and Deris, M.M.: Tracing Significant Information using Critical Least Association Rules Model. International Journal of Innovative Computing and Applications, Inderscience, 5, 3-17 (2013)
Baker, R., and Yacef, K.: The State of Educational Data mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1), 3–17 (2010)
Kumar, V., and Chadha, A.: Mining Association Rules in Student’s Assessment Data. International Journal of Computer Science Issues, 9(5), 3, September 2012, 211-216 (2012)
Garcia, E., Romero, C., Ventura, S., and Castro, C.: An Architecture for Making Recommendations to Courseware Authors using Association Rule Mining and Collaborative Filtering. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, 19, 99–132 (2009)
Tair, M.A.A., and El-Halees, AM.:. Mining Educational Data to Improve Students’ Performance: A Case Study. International Journal of Information and Communication Technology Research, 2(2), 140-146 (2012)
Chandra, E., and Nandhini, K.: Knowledge Mining from Student Data. European Journal of Scientific Research, 47(1), 156-163 (2010)
Acknowledgments
The authors would like to thanks Universiti Malaysia Terengganu for supporting this work. The work of Tutut Herawan is supported by Excellent Research Grant Scheme no vote O7/UTY-R/SK/0/X/2013 from Universitas Teknologi Yogyakarta, Indonesia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Singapore
About this paper
Cite this paper
Abdullah, Z., Herawan, T., Mat Deris, M. (2014). Discovering Interesting Association Rules from Student Admission Dataset. In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_16
Download citation
DOI: https://doi.org/10.1007/978-981-4585-18-7_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-4585-17-0
Online ISBN: 978-981-4585-18-7
eBook Packages: EngineeringEngineering (R0)