Clustering educational data

Handbook of Educational Data Mining

Volumn , Issue , 2010, Pages 75-92

  Vellido, Alfredo ^a   Castro, Félix ^a,b   Angela Nebot ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85055600758     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b10274     Document Type: Chapter

References (63)

1. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., and Wirth, R., CRIPS-DM 1.0 step by step data mining guide. CRISP-DM Consortium, August 2000.
2. Castro, F., Vellido, A., Nebot, A., and Mugica, F., Applying data mining techniques to e-learning problems, in Evolution of Teaching and Learning Paradigms in Intelligent Environment, Studies in Computational Intelligence (SCI), Vol. 62, Jain, L.C., Tedman, R.A. and Tedman, D.K. (Eds.), pp. 183-221, 2007.
3. MacQueen, J., Some methods for classification and analysis of multivariate observations, in Fifth Berkeley Symposium on Mathematics, Statistics and Probability, University of California Press, Berkeley, CA, pp. 281-297, 1967.
4. Dunn, J.C., A fuzzy relative of the ISODATA process and its use in detecting compact wellseparated clusters. J. Cybern. 3, 32-57, 1973.
5. Pelleg, D. and Moore, A., Accelerating exact k-means algorithms with geometric reasoning, in The Fifth International Conference on Knowledge Discovery in Databases, AAAI Press, Menlo Park, CA, pp. 277-281, 1999.
6. McLachlan, G.J. and Basford, K.E., Mixture Models: Inference and Applications to Clustering. Marcel Dekker, New York, 1988.
7. Blei, D.M., Ng, A.Y., and Jordan, M.I., Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993-1022, 2003.
8. Oja, M., Kaski, S., and Kohonen, T., Bibliography of self-organizing map (SOM). Neural Comput. Surveys 3, 1-156, 2002.
9. Kohonen, T., Self-Organizing Maps, 3rd edn., Springer-Verlag, Berlin, Germany, 2001.
10. Bishop, C.M., Svensén, M., and Williams, C.K.I., GTM: The generative topographic mapping. Neural Comput. 10(1), 215-234, 1998.
11. Hore, P., Hall, L.O., and Goldgof, D.B., A scalable framework for cluster ensembles. Pattern Recogn. 42(5), 676-688, 2009.
12. Busse, L.M., Orbanz, P., and Buhmann, J.M., Cluster analysis of heterogeneous rank data, in Proceedings of the 24th International Conference on Machine Learning (ICML), ACM, New York, pp. 113-120, 2007.
13. Andrews, N.O. and Fox, E.A., Recent developments in document clustering. Technical Report, TR-07-35. Department of Computer Science, Virginia Tech, Blacksburg, VA, 2007.
14. Jain, A.K., Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651-666, 2010.
15. Tsuda, K. and Kudo, T., Clustering graphs by weighted substructure mining, in Proceedings of the 23rd International Conference on Machine Learning (ICML), ACM, New York, pp. 953-960, 2006.
16. Backstrom, L., Huttenlocher, D., Kleinberg, J., and Lan, X., Group formation in large social networks:Membership, growth and evolution, in Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, 2006.
17. Mao, J. and Jain, A.K., A self-organizing network for hyperellipsoidal clustering (HEC), IEEE Trans. Neural Netw. 7, 16-29, 1996.
18. Ball, G. and Hall, D., ISODATA, a novel method of data analysis and pattern classification. Technical report NTIS AD 699616. Stanford Research Institute, Stanford, CA, 1965.
19. Forgy, E.W., Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications. Biometrics 21, 768-769, 1965.
20. Eschrich, S., Ke, J., Hall, L.O., and Goldgof, D.B., Fast accurate fuzzy clustering through data reduction. IEEE Trans. Fuzzy Syst. 11(2), 262-270, 2003.
21. Witten, I.H. and Eibe, F., Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan Kaufman-Elsevier, San Francisco, CA, 2005.
22. Jain, A.K., Murty, M.N., and Flynn, P.J., Data clustering: A review. ACM Comput. Serv. 31(3), 264-323, 1999.
23. Mitra, S. and Acharya, T., Data Mining: Multimedia, Soft Computing and Bioinformatics. John Wiley & Sons, Hoboken, NJ, 2003.
24. Dempster, A.P., Laird, M.N., and Rubin, D.B., Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39(1), 1-38, 1977.
25. Vellido, A., Missing data imputation through gtm as a mixture of t-distributions. Neural Netw. 19(10), 1624-1635, 2006.
26. Vellido, A., Assessment of an unsupervised feature selection method for generative topographic mapping, in The 16th International Conference on Artificial Neural Networks (ICANN’2006), Athens, Greece, LNCS (4132) Springer-Verlag, Berlin, Germany, pp. 361-370, 2006.
27. Drigas, A. and Vrettaros, J., An intelligent tool for building e-learning contend-material using natural language in digital libraries. WSEAS Trans. Inf. Sci. Appl. 5(1), 1197-1205, 2004.
28. Hammouda, K. and Kamel, M., Data mining in e-learning, in e-Learning Networked Environments and Architectures: A Knowledge Processing Perspective, Pierre, S. (Ed.). Springer-Verlag, Berlin, Germany, 2005.
29. Tane, J., Schmitz, C., and Stumme, G., Semantic resource management for the web: An e-learningapplication, in The Proceedings 13th World Wide Web Conference, WWW2004, Fieldman, S., Uretsky, M. (Eds.). ACM Press, New York, pp. 1-10, 2004.
30. Hwang, G.J., A test-sheet-generating algorithm for multiple assessment requirements. IEEE Trans. Educ. 46(3), 329-337, 2003.
31. Carpenter, G., Grossberg, S., and Rosen, D.B., Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netw. 4, 759-771, 1991.
32. Dreyfus, S.E. and Law, A.M., The Art and Theory of Dynamic Programming, Academic Press, New York, 1977.
33. Kim, J., Chern, G., Feng, D., Shaw, E., and Hovy, E., Mining and assessing discussions on the web through speech act analysis, in The AAAI Workshop on Web Content Mining with Human Language Technology, Athens, GA, pp. 1-8, 2006.
34. Chu, W.W. and Chiang, K., Abstraction of high level concepts from numerical values in databases, in The AAAI Workshop on Knowledge Discovery in Databases, Seattle, WA, July 1994.
35. Pirrone, R., Cossentino, M., Pilato, G., and Rizzo, R., Concept maps and course ontology:A multi-level approach to e-learning, in The II AI*IA Workshop on Artificial Intelligence and E-learning, Pisa, Italy, September 23-26, 2003.
36. Mendes, M.E.S., Martinez, E., and Sacks, L., Knowledge-based content navigation in e-learning applications, in The London Communication Symposium, London, U.K., September 9-10, 2002.
37. Zhuhadar, L. and Nasraoui, O., Personalized cluster-based semantically enriched web search for e-learning, in The ONISW’08, Napa Valley, CA, ACM, New York, pp. 105-111, 2008.
38. Zhuhadar, L. and Nasraoui, O., Semantic information retrieval for personalized e-learning, in The 20th IEEE International Conference on Tools with Artificial Intelligence, IEEE Computer Society, Dayton, OH, pp. 364-368, November 3-5, 2008.
39. Chiu, D.Y., Pan, Y.C., and Chang, W.C., Using rough set theory to construct e-learning FAQ retrieval infrastructure, in The First IEEE International Conference on Ubi-Media Computing, Lanzhou, China, pp. 547-552, July 15-16, 2008.
40. Su, Z., Song, W., Lin, M., and Li, J., Web text clustering for personalized e-learning based on maximal frequent itemsets, in The International Conference on Computer Science and Software Engineering, IEEE Computer Science, Hubei, China, pp. 452-455, December 12-14, 2008.
41. Mullier, D., A tutorial supervisor for automatic assessment in educational systems. Int. J. e-Learn. 2(1), 37-49, 2003.
42. Mullier, D., Moore, D., and Hobbs, D., A neural-network system for automatically assessing students, in World Conference on Educational Multimedia, Hypermedia and Telecommunications, Kommers, P. and Richards, G. (Eds.), AACE, Chesapeake, VA, pp. 1366-1371, 2001.
43. Tang, T.Y. and McCalla, G., Smart recommendation for an evolving e-learning system:Architecture and experiment. Int. J. e-Learn. 4(1), 105-129, 2005.
44. McCalla, G., The ecological approach to the design of e-learning environments: Purposebased capture and use of information about learners. J. Int. Media Educ., Special Issue on the Educational Semantic Web, 2004.
45. Tai, D.W.S., Wu, H.J., and Li, P.H., A hybrid system: Neural network with data mining in an e-learning environment, in KES’2007/WIRN’2007, Part II, B. Apolloni et al. (Eds.), LNAI 4693, Springer-Verlag, Berlin, Germany, pp. 42-49, 2007.
46. Castro, F., Vellido, A., Nebot, A., and Minguillón, J., Finding relevant features to characterize student behavior on an e-learning system, in The International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS’05), Hamid, R.A. (Ed.), Las Vegas, NV, pp. 210-216, 2005.
47. Castro, F., Vellido, A., Nebot, A., and Minguillón, J., Detecting atypical student behaviour on an e-learning system, in VI Congreso Nacional de Informática Educativa, Simposio Nacional de Tecnologías de la Información y las Comunicaciones en la Educación (SINTICE’2005), Granada, Spain, pp. 153-160, 2005.
48. Vellido, A., Castro, F., Nebot, A., and Mugica, F., Characterization of atypical virtual campus usage behavior through robust generative relevance analysis, in The 5th IASTED International Conference on Web-Based Education (WBE’2006), Puerto Vallarta, Mexico, pp. 183-188, 2006.
49. Teng, C., Lin, C., Cheng, S., and Heh, J., Analyzing user behavior distribution on e-learning platform with techniques of clustering, in Society for Information Technology and Teacher Education International Conference, Atlanta, GA, pp. 3052-3058, 2004.
50. Talavera, L. and Gaudioso, E., Mining student data to characterize similar behavior groups in unstructured collaboration spaces, in Workshop in Artificial Intelligence in Computer Supported Collaborative Learning, in conjunction with 16th European Conference on Artificial Intelligence (ECAI’2003), Valencia, Spain, pp. 17-22, 2004.
51. Christodoulopoulos, C.E. and Papanikolaou, K.A., A group formation tool in a e-learning context, in The 19th IEEE International Conference on Tools with Artificial Intelligence, IEEE Computer Society, Patras, Greece, pp. 117-123, October 29-31, 2007.
52. Tian, F., Wang, S., Zheng, C., and Zheng, Q., Research on e-learner personality grouping based on fuzzy clustering analysis, in The 12th International Conference on Computer Supported Cooperative Work in Design (CSCWD’2008), Xi’an, China, pp. 1035-1040, April 16-18, 2008.
53. Lu, F., Li, X., Liu, Q., Yang, Z., Tan, G., and He, T., Research on personalized e-learning system using fuzzy set based clustering algorithm, in ICCS’2007, Y. Shi et al. (Eds.), Part III, LNCS (4489), Springer-Verlag, Berlin, Germany, pp. 587-590, 2007.
54. Zhang, K., Cui, L., Wang, H., and Sui, Q., An improvement of matrix-based clustering method for grouping learners in e-learning, in The 11th International Conference on Computer Supported Cooperative Work in Design (CSCWD’2007), Melbourne, Australia, pp. 1010-1015, April 26-28, 2007.
55. Mylonas, P., Tzouveli, P., and Kollias, S., Intelligent content adaptation in the framework of an integrated e-learning system, in The International Workshop in Combining Intelligent and Adaptive Hypermedia Methods/Techniques in Web Based Education Systems (CIAH’2005), Salzburg, Austria, pp. 59-66, September 6-9, 2005.
56. Tang, T.Y. and Chan, K.C., Feature construction for student group forming based on their browsing behaviors in an e-learning system, in PRICAI’2002, Ishizuka, M. and Sattar, A. (Eds.), LNAI (2417), Springer-Verlag, Berlin, Germany, pp. 512-521, 2002.
57. Manikandan, C., Sundaram, M.A.S., and Mahesh, B.M., Collaborative e-learning for remote education-an approach for realizing pervasive learning environments, in 2nd International Conference on Information and Automation (ICIA’2006), Colombo, Srilanka, pp. 274-278, December 14-17, 2006.
58. Khalil, F., Li, J., and Wang, H., Integrating recommendation models for improved web page prediction accuracy, in The 31st Australasian Computer Science Conference (ACSC’2008), Conference in Research and Practice in Information Technology (CRPIT’2008), Vol. 74, Wollongong, Australia, pp. 91-100, 2008.
59. Zakrzewska, D., Using clustering techniques for students’ grouping in intelligent e-learning systems, in USAB 2008, Holzinger, A. (Ed.), LNCS (5298), Springer-Verlag, Berlin, Germany, pp. 403-410, 2008.
60. Yang, Q., Zheng, S., Huang, J., and Li, J., A design to promote group learning in e-learning by naive Bayesian, rComput. Intel. Des. 2, 379-382, 2008.
61. Fu, H., and Foghlu, M.O., A conceptual subspace clustering algorithm in e-learning, in The 10th International Conference on Advances in Communication Technology (ICACT’2008), IEEE Conference Proceeding, Phoenix Park, Republic of Korea, pp. 1983-1988, 2008.
62. Chan, C., A Framework for assessing usage of web-based e-learning systems, in The Second International Conference on Innovative Computing, Information and Control (ICICIC’2007), Kumamoto, Japan, pp. 147-150, September 5-7, 2007.
63. Myszkowski, P.B., Kwasánicka, H., and Markowska, U.K., Data mining techniques in e-learning CelGrid system, in The 7th Computer Information Systems and Industrial Management Applications, IEEE Computer Science, Ostrava, the Czech Republic, pp. 315-319, June 26-28, 2008.