Abstract
A new application of data mining to the problem of University dropout is presented. A new modeling technique, based on Markov chains, has been developed to mine information from data about the University students’ behavior. The information extracted by means of the proposed technique has been used to deeply understand the dropout problem, to find out the high-risk population and to drive the design of suitable politics to reduce it. To represent the behavior of the students the available data have been modeled as a Markov chain and the associated transition probabilities have been used as a base to extract the aforesaid behavioral patterns. The developed technique is general and can be successfully used to study a large range of decisional problems dealing with data in the form of events or time series. The results of the application of the proposed technique to the students’ data will be presented.
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Keywords
- Markov Chain
- Association Rule
- Matriculation Date
- Markov Chain Monte Carlo Method
- Probabilistic Inference
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References
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741 (1984)
Gelfand, A.E., Smith, A.F.M.: Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398–409 (1990)
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, E.: Equation of state calculations by fast computing machines. Journal of Chemical Physics 21, 1087–1092 (1953)
Hastings, W.K.: Monte Carlo sampling method using Markov chains and their applications. Biometrika 57, 97–109 (1970)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. of VLDB Conference, Santiago, Chile (1994)
Mannila, H., Toivonen, H., Verkamo, I.: Efficient Algorithms for discovering association rules. In: KDD 1994: AAAI Workshop on Knowledge Discovery in Databases (1994)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. IBM Research Report (1995)
Howard, R.A.: Dynamic programming and Markov processes. John Wiley, Chichester (1960)
Neal, R.: Probabilistic Inference using Markov Chain Monte Carlo Methods. Dept. of Computer Science, University of Toronto (1993)
Diaconis, P., Stroock, D.: Geometric Bounds for eigenvalues of Markov Chains. Annals of Applied Probability 1, 36–61 (1991)
Taha, H.A.: Operation Research, pp. 400–406. Macmillan Publisher, Basingstoke (1971)
Howard, R.A.: Dinamic Programming and Markov Processes. Wiley, Chichester (1960)
Arnold, S.F.: Gibbs Sampling. Handbook of Statistics 9, 599–626 (1993)
Chib, S., Greenberg, E.: Understanding the Metropolis-Hastings Algorithm. The American Statistician 49(#4), 329–335 (1995)
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© 1999 Springer-Verlag Berlin Heidelberg
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Massa, S., Puliafito, P.P. (1999). An Application of Data Mining to the Problem of the University Students’ Dropout Using Markov Chains. In: Żytkow, J.M., Rauch, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1999. Lecture Notes in Computer Science(), vol 1704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48247-5_6
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DOI: https://doi.org/10.1007/978-3-540-48247-5_6
Publisher Name: Springer, Berlin, Heidelberg
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