<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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. </p><p>The objective of our research is to compare the preprocessing techniques and specialised algorithms and to find their most advantageous usage.</p></abstract>

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.

Comparison of Algorithms for Clustering Incomplete Data

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

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Comparison of Algorithms for Clustering Incomplete Data"}

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...

Comparison of Algorithms for Clustering Incomplete Data

Publié en ligne: 30 mai 2014

Pages: 107 - 127

Reçu: 01 mai 2013

DOI: https://doi.org/10.2478/fcds-2014-0007

Mots clés
clustering, incomplete data, missing value, marginalisation, imputation, IFCM, OCS, NPS, NCS

© A. Matyja, K. Siminski

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Comparison of Algorithms for Clustering Incomplete Data

Publié en ligne: 30 mai 2014

Pages: 107 - 127

Reçu: 01 mai 2013

DOI: https://doi.org/10.2478/fcds-2014-0007

Mots clésclustering, incomplete data, missing value, marginalisation, imputation, IFCM, OCS, NPS, NCS

© A. Matyja, K. Siminski

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
clustering, incomplete data, missing value, marginalisation, imputation, IFCM, OCS, NPS, NCS