Marcin Pełka

Marcin Pełka
Address
Personal information
Experience
2002-2007 PhD student at Wroclaw Univeristy of Economics
2007-2008 assistant at Department of Econometrics and Computer Science,
Wroclaw University of Economics (as master of economics)
Since 2008 assistant at Department of Econometrics and Computer Science,
Wroclaw University of Economics (as PhD in economics)
Education
1997-2002 Wroclaw University of Economics, master of economics,
specialization Banking and Insurance
1999 “CyberSkills” certificate
2002 – 2007 PhD student at Wroclaw Univeristy of Economics
2007 PhD in economics, thesis “Symbolic data analysis and its application
in marketing”
From 2008 assistant at Department of Econometrics and Computer Science,
Wroclaw University of Economics
Lectures
14th-16th of May 2013 – lectures on “Ensemble learning for symbolic data” at
Univeristy of Porto
14th-23rd of May 2014 – lectures on “Ensemble learning for classical data”,
“Conjoint analysis” and “Latent class analysis” at University of Cagliari
Other info
driving license – category “B”
“CyberSkills” certificate
R packages
co-author of symbolicDA package of R (maintainer: Andrzej Dudek)
Most important articles / book chapters
1. Pełka M. (2010), Symbolic multidimensional scaling versus noisy variables and outliers
[In:] H. Locarek-Junge, C. Weihs (Eds.), Classification as a tool for research, SpringerVerlag, Berlin-Heidelberg, p. 341-350.
2. Gatnar E., Walesiak M. (red.) (2011), Analiza danych jakościowych i symbolicznych
z wykorzystaniem programu R [Analysis of qualitative and symbolic data with application
of R software]. C.H. Beck, Warszawa. Co-author of two chapters: Chapter 11:
Multidimensional scaling of qualitative and symbolic data (co-author Artur Zaborski).
Chapter 13: Discriminant analysis and classification trees for symbolic data (co-author
Andrzej Dudek).
3. Pełka M. (2012), Ensemble approach for clustering of interval-valued symbolic data.
“Statistics in Transition”, Volume 13, Number 2, p. 335-342.
4. Pełka M. (2012), Skalowanie wielowymiarowe i klasyfikacja danych symbolicznych
w ocenie pozycji produktów na rynku [Multidimensional scaling and clustering of symbolic
data in brand positioning]. „Marketing i Rynek”, no. 3/2012, p. 21-26.
5. Pełka M. (2013), Clustering of symbolic data with application of ensemble approach. “Acta
Universitatis Lodziensis. Folia Oeconomica”, no. 285, p. 89-95.
6. Pełka M., Zaborski A. (2013), Unfolding analysis adaptation for symbolic data – hybrid
and symbolic-numeric approach, “Ekonometria”, 3(41), p. 32-39.
7. Pełka M. (2013), Podejście wielomodelowe analizy danych symbolicznych w ocenie pozycji
produktów na rynku [Ensemble learning for symbolic data in product positioning],
”Ekonometria”, 2(40), p. 95-102.
8. Pełka M. (2014), Symbolic cluster ensemble based on co-association matrix versus noisy
variables and outliers, [In:] Spiliopoulou M., Schmidt-Thieme L., Janning R. (Eds.), Data
analysis, machine learning and knowledge discovery, Springer-Verlag, Berlin-Heidelberg,
p. 209-216.
9. Pełka M. (2014), Klasyfikacja pojęciowa danych symbolicznych w podejściu
wielomodelowym [Conceptual clustering of symbolic data in ensemble approach].
”Research Papers of Wroclaw University of Economics”, no. 327, p. 202-209.
10. Baier D., Pełka M., Rybicka A., Schreiber S. (2014), TCA/HB Compared to CBC/HB for
Predicting Choices Among Multi-Attributed Products. Archives of Data Science Series A
(Online-First), vol. 1, no. 1 [URL:]
http://www.em.uni-karlsruhe.de/goto/aods/series_a/articles.html