000			01821nam a2200265 a 4500
001			vtls000081662
003			KUKTEM
005			20251117113227.0
008			140721t2013 my a f m 000 0 eng d
020			_aTHE0001834(Local)
039		9	_a201905131433 _byusri _y201407211137 _zFida
040			_aUMP
090			_aQA76.76.D47 L65 2013 rs Bc.
100	1		_aLok Leh Leong
245	1	0	_aSoft set approach for decision attribute selection in data clustering / _cLok Leh Leong
260			_aKuantan, Pahang : _bUMP, _c2013
300			_axii, 73 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM
502			_aProject paper (Bachelor of Computer Science (Software Engineering) -- Universiti Malaysia Pahang - 2013
504			_aBibliography : p.69-71
520	3		_aClustering is one of the fundamental operations in data mining that cluster set of heterogeneous data objects into smaller homogeneous classes. Using clustering attribute (decision attribute) is one of the data clustering techniques. Soft set theory is a new mathematical tool applying in clustering applications in databases circumstances. Hence,the research aim is to find the practical technique of soft set theory for decision attribute selection in soft set theory. The test is been done by using two UCI benchmark datasets to determine the speed of execution time for soft set approach with rough set techniques, that are Total Roughness (TR), Min-Min Roughness (MMR) and Maximum Dependency of Attributes (MDA). The results show that the proposed technique provides faster decision for selecting a clustering attribute
650		0	_aComputer software _xDevelopment
650		0	_aApplication software _xDevelopment
650		0	_aData clustering
999			_aVIRTUA40 _c5036 _d5042
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*9992