Learning through data mining

Computer Applications in Engineering Education

Volumn 13, Issue 1, 2005, Pages 60-65

  Žalik, Krista Rizman ^a  

^a UNIVERSITY OF MARIBOR   (Slovenia)

Author keywords

Data mining; Unsupervised learning

Indexed keywords

EDUCATION; GLOBAL SYSTEM FOR MOBILE COMMUNICATIONS; INFORMATION RETRIEVAL; INTERFACES (COMPUTER); NAVIGATION; WEBSITES;

DATA-DRIVEN APPROACH; DATASETS; TESTED PORTALS;

DATA MINING;

EID: 24944557189     PISSN: 10613773     EISSN: None     Source Type: Journal    
DOI: 10.1002/cae.20030     Document Type: Article

References (10)

1. Cross Industry Standard Process for Data Mining, 2003, [Online] http://www.crisp-dm.org/ (Accessed 10.12.2003).
2. Data Mining Group - DMG and PMML specification, http://www.dmg.org/ (Accessed 10.12.2003).
3. L. Devroye, L. Gyprfi, and G. A. Lugosi, Probabilistic theory of pattern recognition, Springer-Verlag, New York, 1997.
4. F. Farnstorm, J. Lewis, and C. Elkan, Scalability for clustering algorithm revisited, SIGKDD explorations (2000), 51-57.
5. J. D. Foley, A. van Dam, S. K. Feiner, and J. F. Hughes, Computer graphics - Principles and practice, 2nd ed., Addison-Wesley, Reading, MA, 1990.
6. J. Han and M. Camber, Data mining: Concepts and techniques, Morgan Kaufman Publishers, San Diego, 2001.
7. T. Hastile, R. Tibshirani, and J. Friedman, The elements of statistical learning: Data Mining, inference and prediction, Springer-Verlag, New York, 2001.
8. L. Kaufman and P. J. Rousseeuw, Finding groups in data: An introduction to cluster analysis, Wiley, New York, 1990.
9. J. MacQueen, Some methods for classification and analysis of multivariate observations, Proc.5. Berkeley Symp Math Statist Prob 1 (1967), 281-297.
10. V. Vapnik, The nature of statistic learning theory, Springer-Verlag, New York, 1996.