Comparison of Algorithms for Clustering Incomplete Data

• Autorzy: Matyja A , Siminski K

Identyfikatory

DOI

10.2478/fcds-2014-0007

• Języki publikacji: EN

Abstrakty

EN

The missing values are not uncommon in real data sets. The algorithms and methods used for the data analysis of complete data sets cannot always be applied to missing value data. In order to use the existing methods for complete data, the missing value data sets are preprocessed. The other solution to this problem is creation of new algorithms dedicated to missing value data sets. The objective of our research is to compare the preprocessing techniques and specialised algorithms and to find their most advantageous usage.

Słowa kluczowe

EN

clustering; incomplete data; missing value; marginalisation; imputation; IFCM; OCS; NPS; NCS

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2014
• Tom: Vol. 39, No. 2
• Strony: 107--127
• Opis fizyczny: Bibliogr. 27 poz.

Twórcy

autor

Matyja A

• Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Siminski K

• Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

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