Warianty tytułu
Determining the Number of Clusters in Binary Data Sets
Języki publikacji
Abstrakty
Poddano analizie 6 indeksów ustalania optymalnej liczby skupień w binarnych zbiorach danych. Ocenie poddano międzyinnymi następujące indeksy ustalania optymalnej liczby skupień: indeks Scott`a - Symons`a, indeks Marriot`a i 4 wiarianty indeksu Fierdnan`a - Rubin`a.
In this paper the performance of six 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.: Scott-Symons, Marriot and 4 variant's of Friedman-Rubin Index. For the evaluation of the performance of the indexes, k-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. (original abstract)
Rocznik
Numer
Strony
203-210
Opis fizyczny
Twórcy
autor
Bibliografia
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- Najman K., Najman K. (2006), Analityczne metody ustalania liczby skupień w rozmytych zbiorach danych. Prace Naukowe Akademii Ekonomicznej we Wrocławiu, Taksonomia 13: Klasyfikacja i analiza danych - teoria i zastosowania.
- Scott A.J., Symons M.J. (1971), Clustering methods based on likelihood ratio criteria. Biometrics, 27, 387-397.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000150760491