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Hybrid soft set-based reduction and its application for fruit anthracnose decision making / Mohamad Reza Abd Rahiman

By:

Mohamad Reza Abd Rahiman

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2012Description: xii, 69 p. : ill. (some col.) ; 30 cm. + 1 CD-ROMISBN:

THE0001853(Local)

Subject(s):

Dissertation note: Project paper (Bachelor of Computer Science (Computer System and Networking) -- Universiti Malaysia Pahang - 2012 Abstract: Soft set theory widely use in data reduction that help to reduce data and decision making process are more efficient. Original data that collected usually large and more importantly whole amount of data that been collected are not necessary for decision making. Data need to be reduced from its original size for data transformation. This project will focus on hybrid reduction of soft set and its application. A new method in Boolean data reduction and decision making is presented. The common research are in reduction of attribute and concentrate on columns as dispensable attribute, while hybrid use object and parameter reduction that give the higher percentage of reduction. To this, it provide better results in reducing the size of datasets.

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Holdings ( 2 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	CD 7633 \| QA76.76.D47 R49 2012 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000078909
Final Year Report	UMPLIB PEKAN	QA76.76.D47 R49 2012 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000078908

Project paper (Bachelor of Computer Science (Computer System and Networking) -- Universiti Malaysia Pahang - 2012

Bibliography : p.55-60

Soft set theory widely use in data reduction that help to reduce data and decision making process are more efficient. Original data that collected usually large and more importantly whole amount of data that been collected are not necessary for decision making. Data need to be reduced from its original size for data transformation. This project will focus on hybrid reduction of soft set and its application. A new method in Boolean data reduction and decision making is presented. The common research are in reduction of attribute and concentrate on columns as dispensable attribute, while hybrid use object and parameter reduction that give the higher percentage of reduction. To this, it provide better results in reducing the size of datasets.