Thesis supervisor: Márton Ispány
co-supervisor: László Szathmáry
Location of studies (in Hungarian): Debreceni Egyetem Informatikai Kar Abbreviation of location of studies: DE IK
Description of the research topic:
Development and investigation of a new data mining models or modification of existing ones which can successfully be applied in various fields. The optional subthemes include both supervised and unsupervised models. The supervised data mining models are regression models and regularization, kernel method and radial basis functions, sparse kernels (SVM and RVM), graphical models and Bayesian networks, high-dimensional problems. Non-supervised data mining models: mixtures and the EM algorithm, clustering, Kohonen's nets, principal component analysis and singular valued decomposition, non-negative matrix factorization, independent component analysis, multidimensional scaling. The research topics also include frequent or rare itemset mining, exploring the association rules and analysis of sequential data (eg. time series). The developed models are tested on large datasets.
Bibliography
Bishop, C. M., Pattern Recognition and Machine Learning, Springer, 2006.
Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, 2009.
Neapolitan, R.E., Learning Bayesian Networks, Pearson Prentice Hall, 2004.
Feldman, R., Sanger, J., The Text Mining Handbook. Advanced Approaches in Analyzing Unstructured Data. Cambridge, 2006.
Liu, Bing, Web Data Mining, Exploring Hyperlinks, Contents, and Usage Data, Springer 2011.
Recommended language skills (in Hungarian): angol Number of students who can be accepted: 1
Deadline for application: 2018-02-16
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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