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.
| Original language | English |
|---|---|
| Pages (from-to) | 107-127 |
| Number of pages | 21 |
| Journal | Foundations of Computing and Decision Sciences |
| Volume | 39 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Jun 2014 |
Keywords
- IFCM
- NCS
- NPS
- OCS
- clustering
- imputation
- incomplete data
- marginalisation
- missing value
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science
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