Finding the Key Influences on the House Price by Finite Mixture Model based on the Real Estate Data in Changchun
Nowadays it's difficult for us to analyze the development law of real estate. What's more, predictable house price and understandable key influences can also build a healthier real estate market. Therefore, we propose a model which can predict the house price, while it can find key influences which are important influences on the house price. Our method is inspired by the finite mixture model (FMM) and information gain ratio (IGO). Specifically, we collect data that includes detail information about houses and communities from Anjuke Inc. which is an online platform for house sales. Then, according to the data, we find the scope of latent groups number by cluster methods to avoid blind searching the number of latent groups. Next, we use IGO to rank the features and weight them and we build a regression model based on the finite mixture model. Finally, the experimental results demonstrate our method performance on predicting house price, and we find key influences on house price.
X. Xu et al., "Finding the Key Influences on the House Price by Finite Mixture Model based on the Real Estate Data in Changchun," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11448 LNCS, pp. 378-382, Springer Verlag, Apr 2019.
The definitive version is available at https://doi.org/10.1007/978-3-030-18590-9_49
24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 (2019: Apr. 22-25, Chiang Mai, Thailand)
Keywords and Phrases
Expectation Maximization Algorithm; Finite mixture model; Information gain ratio; Real estate
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International Standard Serial Number (ISSN)
Article - Conference proceedings
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