Porównanie stabilności zagregowanych algorytmów taksonomicznych opartych na idei metody bagging

• Autorzy: Dorota Rozmus

Warianty tytułu

Comparison of Stability of Cluster Ensembles based on Bagging Idea

• Języki publikacji: PL

Abstrakty

W niniejszym badaniu uwaga zostanie skupiona na stabilności metod taksonomicznych. Głównym celem tego artykułu jest porównanie stabilności zagregowanych algorytmów taksonomicznych, a także relacji między stabilnością i dokładnością; przy czym pod uwagę zostanie wzięta tylko specyficzna klasa metod agregacji, które są oparte na idei metody bagging. (fragment tekstu)

EN

Ensemble approach has been successfully applied in the context of supervised learning to increase the accuracy and stability of classification. One of the most popular method is bagging based on bootstrap samples. Recently, analogous techniques for cluster analysis have been suggested in order to increase classification accuracy, robustness and stability of the clustering solutions. Research has proved that, by combining a collection of different clusterings, an improved solution can be obtained. A desirable quality of the method is the stability of a clustering algorithm with respect to small perturbations of data (e.g., data subsampling or resampling, small variations in the feature values) or the parameters of the algorithm (e.g., random initialization). Here, we look at the stability of the ensemble and carry out an experimental study to compare stability of cluster ensembles based on bagging idea. (original abstract)

Słowa kluczowe

PL

Metody taksonomiczne; Agregacja modeli; Algorytmy; Metody samowsporne

EN

Taxonomic methods; Aggregation models; Algorithms; Bootstrap

• Czasopismo: Studia Ekonomiczne / Uniwersytet Ekonomiczny w Katowicach
• Rocznik: 2013
• Numer: nr 133 Metody wnioskowania statystycznego w badaniach ekonomicznych
• Strony: 119--134

Twórcy

autor

Dorota Rozmus

• Uniwersytet Ekonomiczny w Katowicach

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