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Approach to Missing Data Recovery

Authors: Xu E; Shaocheng Tong; Yuan Wang; Shang Xu; Peng Li;

Approach to Missing Data Recovery

Abstract

In order to recover the missing data in an information system, the paper proposed a new approach based on rough set to reduce the redundant attributes, discretize the continuous attributes and fill in the missing data. According to indiscernible relationship, discernible vector were defined and used the discernible vector addition rule to reduce attributes. And then, depending on the concept of super-club data and entropy of the information table, discretization of the continuous attributes was implemented. Finally, by use of the corresponding relationship of condition attributes and decision attributes, the definition of interval value and interval value addition rule were defined and filled up the incomplete data. The illustration and experimental results indicate that the approach is effective and efficient.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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