A Prediction Model for Forecasting the Trend of Macau Property Price Movements and Understanding the Influential Factors
Simon Fong1; Yap Bee Wah2
2013-05-01
Source PublicationJournal of Emerging Technologies in Web Intelligence
ISSN1798-0461
Volume5Issue:2Pages:122-131
Abstract

Property price and transaction activities may contain many factors. Data mining for property research provides a feasible way to analyze the trend and to understand the underlying influential factors. This paper addresses the issues and techniques on experiences in data mining for Macau property market. The original data for property price and factors are obtained as a multi-attribute dataset from the Statistics and Census Service of Macao SAR Government. The challenge is to apply different data mining methods and algorithms which include SVM, Neural Network, CandR Tree, Weka, SPSS, Multilayer Perception Model in order to identify hidden knowledge.

KeywordCandr Tree Data Mining Multilayer Perception Model Neural Network Spss Svm Weka
DOIhttps://doi.org/10.4304/jetwi.5.2.122-131
URLView the original
Indexed By其他
Language英语
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Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Faculty of Science and Technology, University of Macau, Macau SAR
2.Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Simon Fong,Yap Bee Wah. A Prediction Model for Forecasting the Trend of Macau Property Price Movements and Understanding the Influential Factors[J]. Journal of Emerging Technologies in Web Intelligence,2013,5(2):122-131.
APA Simon Fong,&Yap Bee Wah.(2013).A Prediction Model for Forecasting the Trend of Macau Property Price Movements and Understanding the Influential Factors.Journal of Emerging Technologies in Web Intelligence,5(2),122-131.
MLA Simon Fong,et al."A Prediction Model for Forecasting the Trend of Macau Property Price Movements and Understanding the Influential Factors".Journal of Emerging Technologies in Web Intelligence 5.2(2013):122-131.
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