@misc{Migdał-Najman_Kamila_Analiza_2008,
 author={Migdał-Najman, Kamila},
 year={2008},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu = Research Papers of Wrocław University of Economics; 2008; Nr 7, s. 264-275},
 language={pol},
 abstract={The article is mainly designed to study the effect of joining the hierarchical agglomerative clustering with the neural network type of SOM (Self Organizing Map), k-means algorithm and k-medoids algorithm. First, the original data set is represented using a smaller set of prototype clusters through neurons SOM, centroids and medoids which allow the efficient use of hierarchical agglomerative clustering to divide the prototypes into groups. The reduction of the computational cost is especially important for hierarchical algorithms allowing clusters of arbitrary size and shape. Second, the hybrid methods allow a rough visual presentation, classify original data to clusters and interpretation of the clusters. The clustering results using hybrid methods as an intermediate step were also comparable with the results obtained directly from the data.},
 title={Analiza porównawcza struktur hierarchicznych skupień uzyskanych z wykorzystaniem hybrydowych metod grupowania},
 type={artykuł},
}