The Design And Implementation Of House Leasing Management System Based On Data Mining

Urban public rental housing provides house insurance for many low-income families and low-income migrants.In order to improve the quality and efficiency of house leasing management,this paper uses house leasing information management and applies data mining technology to improve the matching degree between the supply and demand sides and enhance the effectiveness of house leasing.On the basis of reviewing and analyzing the research background at home and abroad,this paper adopts software engineering ideas to carry out demand analysis,system design,system realization and system testing of the system.First,clarify the application requirements of the system through system analysis,determine the user authority settings of the system,and set the business process of each functional module of the rental management system.Secondly,on the basis of the system network topology design,the system logic design is carried out,and the system function is planned into six parts,which correspond to the corresponding function modules,and each module is composed of several sub-function modules.The detailed design of the system determines and gives the functional timing of the system functional modules,straighten out the logical relationship between the modules,and determine the logical functions of each module to ensure the effective progress of the system development.The system database design gives the E-R diagram of the system database to determine the structure of each database table required by the system.The system implementation adopts the MVC threetier architecture,the system architecture adopts the B/S architecture,the development framework is SSH,and the system functions are realized with JSP and My SQL database.In the construction of a data recommendation system based on data mining technology,the system first collects data through web crawler technology,and cleans data through data preprocessing to achieve data screening,missing values and noise processing.Secondly,through data integration,data transformation and data quantification,a data set suitable for data clustering is obtained.In order to improve the efficiency of the clustering algorithm,multi-dimensional spatial data points are aggregated and stored through the selection of data specifications and clustering attributes clipping.Analyze attribute data on multiple abstract levels of concept hierarchy,and classify the acquired house data through the K-Means clustering algorithm,thereby extracting and abstracting the user's interest model.On this basis,in order to achieve the matching of user interests and house attributes,through gray correlation analysis,the correlation between multi-attribute sample data and multi-dimensional user interest models is obtained,so as to achieve effective matching of house data and target users to improve the system matching.The system test results show that the housing rental management system designed and implemented in this paper can meet the predetermined application requirements,and the user recommendation function implemented by data mining technology can effectively improve the matching degree between the supply and demand sides..