Research On Location Selection Of Hot Pot Chain Stores Based On Data Mining

With the rapid development of economy and increasingly fierce competition among enterprises,suitable store location is one of the most important factors affecting the development of enterprises,which can lay a solid foundation for the later development.Because chain stores have the advantages of sharing popularity and reputation,businesses will be preferred to operate chain stores.At present,there are few literatures related to the location of chain stores and there are many contents which need to be improved in the proposed research methods.Therefore,this thesis refines the research object to hotpot chain stores and discusses how to develop a more perfect and more accurate hotpot chain store location model.Based on data mining,this thesis studies the location model.In the process of establishing the evaluation index system,the initial evaluation index system is firstly proposed,which is composed of 5 first-level indexes and 15 second-level indexes.The first-level indicators are divided into population factor,traffic factor,competitive factor,self factor and other factors.The minimum circle coverage algorithm,latitude and longitude distance formula,clustering analysis and kruskal-wallis rank sum test are used in the subdivision of the first-level indexes.Then,this thesis uses the feature selection algorithm in caret package and boruta package to select the features in initial evaluation index system.The results of feature selection are that caret package retains 11 features and boruta package retains 12 features.Three different evaluation index systems are determined,and the location model of hotpot chain stores is constructed and verified based on wuhan regional data and nanjing regional data.Research conclusions of this thesis are:(1)Logistic regression,neural network and support vector machine are used for modeling based on the three evaluation index systems.After summarizing the modeling results,the model with the highest accuracy is obtained.The accuracy of the model is 92.5% and the recall rate is 75.8%.The algorithm used in the model is support vector machine,and the kernel function is polynomial kernel function.The evaluation index system consists of 5 first-level indicators and 12 second-level indicators.(2)After the hotpot chain location model is determined,the performance of the model is further verified with the data of nanjing region.The results of model validation show that the accuracy of hotpot chain location model is 90.3%,indicating that the hotpot chain location model proposed in this thesis has high accuracy and practicability.The innovation of this thesis lies in improving and enriching the evaluation index system of chain store location,adding self factor and competitive factor,and building the hotpot chain location model based on the optimized evaluation index system.The model has high accuracy and wide application range.The research method and results of this thesis extend the idea of site selection research and have practical application value.