A Comparison of Efficiency in Classifying Gaming Data using Data Mining Techniques

Please use this identifier to cite or link to this item: https://publication.npru.ac.th/jspui/handle/123456789/810

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DC Field	Value	Language
dc.contributor.author	Hmeanaui, Thiraporn	-
dc.contributor.author	Pechkonchom, Taveesa	-
dc.contributor.author	Lukkananuruk, Nitima	-
dc.contributor.author	Hengpraphorm, Kairung	-
dc.contributor.author	Hengpraphorm, Supojn	-
dc.date.accessioned	2020-10-12T07:26:11Z	-
dc.date.available	2020-10-12T07:26:11Z	-
dc.date.issued	2020-07-10	-
dc.identifier.uri	https://publication.npru.ac.th/jspui/handle/123456789/810	-
dc.description.abstract	The aim of this research is to compare the efficiency of the suitable technique for gaming data classification. In the experiment, Weka 3.8.4 software is used with two data classification techniques: J48 and JRip to test with 3,196 gaming data with 37 features. The experimental results show that J48 gives the best result of the classification accuracy (99.43%). The second technique is JRip with the classification accuracy = 99.19% respectively. Therefore, J48 is the suitable technique for gaming data classification.	en_US
dc.subject	Feature	en_US
dc.subject	Classification	en_US
dc.subject	Decision Tree	en_US
dc.subject	J48	en_US
dc.subject	JRIP	en_US
dc.title	A Comparison of Efficiency in Classifying Gaming Data using Data Mining Techniques	en_US
Appears in Collections:	Proceedings of the 12th NPRU National Academic Conference

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