An approach to unsupervised classification

• Autorzy: Przybyła T. , Pander T. , Horoba K. , Kupka T. , Matonia A.
• Języki publikacji: EN

Abstrakty

EN

Classification methods can be divided into supervised and unsupervised methods. The supervised classifier requires a training set for the classifier parameter estimation. In the case of absence of a training set, the popular classifiers (e.g. K-Nearest Neighbors) can not be used. The clustering methods are considered as unsupervised classification methods. This paper presents an idea of the unsupervised classification with the popular classifiers. The fuzzy clustering method is used to create a learning set. The learning set includes only these patterns that are the best representative of each class in the input dataset. The numerical experiment uses an artificial dataset as well as the medical datasets (PIMA, Wisconsin Breast Cancer) and illustrates the usefulness of the proposed method.

Słowa kluczowe

EN

classification; fuzzy clustering; unsupervised classification; nearest neighbour classifier

PL

klasyfikacja; grupowanie rozmyte; klasyfikacja nienadzorowana; klasyfikator najbliższych sąsiadów

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2011
• Tom: Vol. 17
• Strony: 105--111
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Przybyła T.

• Silesian University of Technology, Institute of Electronics, Akademicka St. 16, 44-100 Gliwice, Poland

autor

Pander T.

autor

Horoba K.

autor

Kupka T.

autor

Matonia A.

Bibliografia

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