Sequential Pattern Data Mining Algorithm and Blockchain Technology for Preparing a Housing Price Prediction Model

【Author】 Zou, Yuntao; Li, Jiang

【Source】IETE JOURNAL OF RESEARCH

【影响因子】1.877

【Abstract】The current housing price trend prediction model ignores the selection of housing price influencing factors and cannot accurately obtain the price value of data mining results, which leads to a large price prediction error and overconsumption of time. Therefore, a new housing price trend prediction model based on a sequential modular F-type data mining algorithm is designed which can collect information through a blockchain environment. This paper selects location grade, traffic condition, interior area, building structure, building time, and building quality as parameters and generalizes them. By using the method of correlation analysis, we can find out the attributes with a high degree of correlation and complete the variable selection of influencing factors of housing price. Using the housing price sequence model, the characteristics of housing price data are extracted and the data information entropy is obtained. The information entropy is used to reduce the dimension of data space and complete the prediction of house prices using data mining. The phase space of the housing price time series is reconstructed, and the housing price trend prediction model is constructed. The experimental results show that the housing price data mining time of the designed model is shorter, and the price trend prediction accuracy is higher. The model is versatile in adaptability due to the incorporation of the blockchain mechanism in the proposed model.

【Keywords】Association analysis; Blockchain; Data mining algorithm; House price; Sequence pattern; Trend forecast; Variable screening

【发表时间】

【收录时间】2022-03-09

【文献类型】期刊

【主题类别】

区块链应用-金融领域-