01821nam a2200265 a 4500 vtls000081662 KUKTEM 20251117113227.0 140721t2013 my a f m 000 0 eng d THE0001834(Local) 201905131433 yusri 201407211137 Fida UMP QA76.76.D47 L65 2013 rs Bc. Lok Leh Leong Soft set approach for decision attribute selection in data clustering / Lok Leh Leong Kuantan, Pahang : UMP, 2013 xii, 73 p. : ill. (some col.) ; 30 cm. + 1 CD-ROM Project paper (Bachelor of Computer Science (Software Engineering) -- Universiti Malaysia Pahang - 2013 Bibliography : p.69-71 Clustering 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 Computer software Development Application software Development Data clustering 0 0 0 1 20000 20000 2019-09-04 0 QA76.76.D47 L65 2013 rs Bc. 0000087055 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 10000 10000 2019-09-04 0 CD 8312 | QA76.76.D47 L65 2013 rs Bc. 0000087056 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 5036 5042 VTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*9992