1ª parte
Apresentação do Instituto de Matemática e Informática de Vilnus
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2ª parte
Integration of Data Mining Methods for Various Applications
Abstract: Nowadays huge amounts of data have been collected. However
comprehension of these data remains a topic problem so far. It is very
difficult for a human being to make use of such an amount of information and
to be able to find basic patterns, relations, or trends in the data. In such
a manner data become ever less and less valuable. Data mining could be
useful for comprehension of data and help to make decisions. The combination
and integrated use of data mining methods (classification, clustering,
visualisation, etc.) of a different nature are under a rapid development.
The combination of different methods can be applied to make a data analysis,
while minimizing the shortcomings of individual methods.
There are investigated following data mining methods:
- dimension reduction methods (principal component analysis,
multidimensional scaling (MDS), manifold learning);
- artificial neural networks (self-organizing maps (Kohonen maps) (SOM),
combination of SOM and MDS algorithm)
- classification methods (support vector machine (SVM), Naive Bayes, k-NN
classifier, classification tree, rule induction)
The great attention is paid to combination of self-organizing and a special
case of multidimensional scaling – Sammon algorithm. This method is applied
to some problem of a different nature: statistical analysis of curricula,
comparison of schools, and analysis of the economic and social conditions of
Central European countries.
Short information about the lecturer: Olga Kurasova is a senior researcher
at Institute of Mathematics and Informatics (IMI), Vilnius, Lithuania. She
also is a senior lecturer of Information Technologies Department at Vilnius
Pedagogical University.
E-mail: Kurasova@ktl.mii.lt Homepage:
http://www.mii.lt/index.php?siteaction=personnel.view&id=380 Webpage of IMI:
http://www.mii.lt
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