Solving Incomplete Datasets in Soft Set Using Supported Sets and Aggregate Values | Zendy

Ahmad Nazari Mohd Rose | Zendy; Hasni Hassan | Zendy; Mohd Isa Awang | Zendy; Nor Aida Mahiddin | Zendy; Hidayatulaminah Mohd Amin | Zendy; Mustafa Mat Deris | Zendy

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Solving Incomplete Datasets in Soft Set Using Supported Sets and Aggregate Values

Author(s) -

Ahmad Nazari Mohd Rose,

Hasni Hassan,

Mohd Isa Awang,

Nor Aida Mahiddin,

Hidayatulaminah Mohd Amin,

Mustafa Mat Deris

Publication year - 2011

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2011.07.046

Subject(s) - computer science , aggregate (composite) , missing data , data mining , set (abstract data type) , value (mathematics) , object (grammar) , data set , artificial intelligence , machine learning , materials science , composite material , programming language

The theory of soft set proposed by Molodtsovin 1999[1]is a new method for handling uncertain data and can be defined as a Boolean-valued information system. Ithas been applied to data analysis and decision support systems based on large datasets. In this paper, it is shown that calculated support value can be used to determine missing attribute value of an object. However, in cases when more than one value is missing, the aggregate values and calculated support values will be used in determining the missing values. By successfully recovering missing attribute values, the integrity of a dataset can still been maintained

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