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Author
Rozmus Dorota (Akademia Ekonomiczna im. Karola Adamieckiego w Katowicach)
Title
Wykorzystanie podejścia zagregowanego w taksonomii
Cluster Ensemble
Source
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu. Taksonomia (15), 2008, nr 7 (1207), s. 330-336, rys., tab., bibliogr. 8 poz.
Issue title
Klasyfikacja i analiza danych - teoria i zastosowania
Keyword
Taksonomia, Algorytmy, Analiza danych
Taxonomy, Algorithms, Data analysis
Note
summ.
Abstract
Zasadniczym celem artykułu jest porównanie zdolności rozpoznawania poprawnej struktury klas uzyskanych za pomocą klasycznych algorytmów taksonomicznych oraz przedstawionego w literaturze podejścia wielomodelowego. (fragment tekstu)

Ensemble methods are used in classification and regression to achieve better prediction accuracy. Recent research reveals that ensemble methods can be used also in taxonomy in order to gain better and more robust objects' classification [Fred, Jain 2005; Kuncheva et al. 2006]. Moreover aggregated approach decreases the risk of gaining a wrong classification because of choosing an unsuitable algorithm. The main aim of the article is to show the possibility of applying one of the most popular ensemble methods, which is bagging [Breiman 1996] in taxonomy. We also show the results of research that main aim was to compare the results of classification with using both classical and ensemble methods with the existing class structure. (original abstract)
Accessibility
The Main Library of the Cracow University of Economics
The Library of University of Economics in Katowice
The Main Library of Poznań University of Economics and Business
The Main Library of the Wroclaw University of Economics
Bibliography
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  1. Bezdek J.C. (1981), Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York.
  2. Blake C., Keogh E., Merz C.J. (1988), UCI Repository of Machine Learning Databases, Department of Information and Computer Science, University of California, Irvine.
  3. Breiman L. (1996), Bagging Predictors, „Machine Learning, 26(2), s. 123-140.
  4. Fred N.L., Jain A.K. (2005), Combining Multiple Clusterings Using Evidence Accumulation, „IEEE Transactions on PAMI", 27(6), s. 835-850.
  5. Gatnar E. (2001), Nieparametryczna metoda dyskryminacji i regresji, Wydawnictwo Naukowe PWN, Warszawa.
  6. Kaufman L., Rousseeuw P.J. (1990), Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York.
  7. Kuncheva L.I., Hadjitodorov S.T., Todorova L.P. (2006), Experimental Comparison of Cluster Ensemble Methods, „Proc FUSION 2006", Florence, Italy.
  8. Leisch F. (1999), Bagged Clustering, Adaptive Information Systems and Modeling in Economics and Management Science, Working Paper 51, SFB.
Cited by
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ISSN
1899-3192
1505-9332
Language
pol
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