@misc{Najman_Krzysztof_Metody_2007,
 author={Najman, Krzysztof},
 year={2007},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 description={Prace Naukowe Akademii Ekonomicznej we Wrocławiu. Taksonomia (14); 2007; nr 1169, s. 321-329},
 language={pol},
 abstract={In this paper the performance of fourteen indexes for determining the number of clusters in a binary data set is analyzed. To ensure that the right number of clusters is known, only artificial sets, designed to simulate data, are used. The resultant optimal clusters have been found to be stable for the different validity indices used, e.g.: Global Silhouette Index, Hubert-Lewin Index, Calinski-Harabasz Index, Ball-Hall Index, Hartigan Index and others. For the evaluation of the performance of the indexes, к-means and hierarchical algorithms are applied. The selection of the number of clusters based on the indexes values for the different number of clusters is done in an automatic way. It was shown that these indexes mightn't support the prediction of the optimal cluster partitioning for those binary data sets.},
 type={artykuł},
 title={Metody ustalania liczby skupień w zbiorach danych binarnych},
}