000			04040nas|a2200457 a 4500
005			20260817163102.0
008			170110s2012 th uu|m rtt 0| a1eng d
035			_a.b12177635
099		9	_aAIT RSPR no.IM-12-07
100	0		_aTanawat Sermvongtrakul
245	1	0	_aApplying decision trees, artificial neural networks and support vector machine to classify the potential of gas stations
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2012
300			_a54 p. : _bill.
490	1		_aResearch studies project report ; _vno. IM-12-07
500			_aA research study submitted in partial fulfillment of the requirements forthedegree of Master of Science inInformation Management, School of Engineering of Technology
502			_aResearch Studies Project Report (M.Sc.) - Asian Institute of Technology, 2012
520			_aIn oil retail industries, gas stations are builtto serve households and industries. An operation of a gas station has a very high risk of loss. To reduce thatrisk, managements need to consider several factors influencing revenues of the station before building a new gas station.With the rapid development in information technology, many different data mining approaches are applied to support management{u2019}s decisions. This study focuses on using several data mining techniques to classify the potential of gas stations. The potential means the capability of growth or being without loss; therefore, this study uses sales volume as an indicator of the potential of gas stations. Using classification techniques in the data mining candiscover some hidden knowledge on existing gas station data and other related information and the knowledgecan also be used for helping the management making a decision for building the new station,which is very beneficial.This study conducted 3 experiments, which use artificial neural networks, support vector machine and decision trees. The results show that using artificial neural networks hasthe highest accuracy in classifying the potential of gas stations. It is more than 85% accuracy for all models. On the contrary, using support vector machine and decision trees, both of them getloweraccuracy rate on testing data. Based on these results,the artificial neural networks technique can serve as a decision support tool for classify a potential of a new gas station, which can reduce the human error in the decision-making process or even help to the management to make a decision with very high accuracy.
650		0	_aNeural networks (Computer science)
650		0	_aDecision trees
650		0	_aDecision support systems
650		0	_aData mining
700	1		_aGuha, Sumanta, _eChairperson
700	0		_aVatcharaporn Esichaikul, _eExamination Committee
700	1		_aDuboz, Raphael, _eExamination Committee
710	2		_aRoyal Thai Government Fellowship, _eScholarship donor
810	2		_aAsian Institute of Technology. _tResearch studies project report ; _vno. IM-12-07
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B01437
907			_a.b12177635 _bmnait _cm
902			_a240409
998			_b0 _c170110 _dm _ea _fm _g0
962			_a000:001:PDF:b1217763:002906:0:0:0:0:0:0 _tAbstract _vn
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