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Data clustering using maximum dependency of attributes and its application to cluster agricultural products / Hafiz Kamal Leang

By:

Hafiz Kamal Leang

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2012Description: xi, 105 p. : ill. (some col.) ; 30 cm. + 1 CD-ROMISBN:

THE0002013(Local)

Subject(s):

Dissertation note: Project paper (Bachelor of Computer Science (Software Engineering)) -- Universiti Malaysia Pahang - 2012 Abstract: This project is about understanding the method of Clustering Data using Rough set Theory. The technique used is Maximum Dependency of attributes. The way this technique work is by calculating the degree of each attribute and selecting the highest dependency based on the degree. The highest degree of attribute will be chosen as the best attribute to be used to cluster the data. A system will be built by using Visual Basic (VB) that will implement this technique to cluster large data faster and easier.

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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	QA278 .H34 2012 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000068745
Final Year Report	UMPLIB GAMBANG	CD 6554 \| QA278 .H34 2012 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000068746

Project paper (Bachelor of Computer Science (Software Engineering)) -- Universiti Malaysia Pahang - 2012

Bibliography : p. 86-91

This project is about understanding the method of Clustering Data using Rough set Theory. The technique used is Maximum Dependency of attributes. The way this technique work is by calculating the degree of each attribute and selecting the highest dependency based on the degree. The highest degree of attribute will be chosen as the best attribute to be used to cluster the data. A system will be built by using Visual Basic (VB) that will implement this technique to cluster large data faster and easier.