The Building Of Credit Card Information Record Platform And The Researching & Applying Of Clustering Method

Living in the 21 st century,we have been in a highly developed era of information science and technology,information science and technology development is extremely fast,especially in economic,political,cultural,military and other aspects of the application is growing,not only information technology makes the human social civilization has made greater progress and the application of the internet financial industry has gradually become widespread.In order to efficiently manage consumer credit card information and improve the efficiency of information processing,credit card information recording platform has been gradually applied to the enterprise credit card information management.But in a wide range of applications of information technology at the same time,the amount of information stored in the database also increased rapidly,the difficulty of extracting efficient information is gradually increasing,which leads to the urgent need for data mining technology applications,it is also very necessary to apply the data mining algorithm to the platform with large amount of information.In recent years,data mining has been gradually applied to various industries,such as internet finance,vehicle network,electronic map navigation applications and other fields.As the forefront of current research projects and very promising technology,data mining not only closely related to database technology,machine learning and other technologies,but also with statistics,probability theory and other basic theory.Clustering analysis algorithm is one of the data mining methods,in recent years,people have made great progress in the research and exploration in this field,different clustering analysis methods have also appeared,and proposed a variety of different ideas based on the clustering method,and to be verified,and give the realization,and finally applied in the appropriate scene.In all fields related to artificial intelligence science and technology,these methods are involved,in the corresponding areas which also achieved good results.However,the current Internet financial industry,there are some problems to be resolved,but also need suitable clustering method to be applied to them.This paper is mainly to build a credit card information recording platform,select,improve the clustering method,and finally its application and platform,which includes the specific content:(1)To build the credit card information recording platform.Based on B / S architecture and Java platform,aiming at the target requirements,the corresponding functional modulesare designed and developed,respectively for the security management,information collection,information management,statistical reporting,information clustering and platform management,support the development of new functional modules on demand.The front end uses HTML,CSS,Java Script to design the page,the back end uses the Java language to write,and uses Struts2,hibernate,spring and so on the frame correlation technology,the database uses the application on the Internet quite broadly and the open source My SQL.(2)The clustering algorithm based on partition is studied deeply.In the design of the information platform,credit points are the primary means of measuring consumer credit,is decremented from the upper limit 100,and is divided as a credit rating every ten points to the lower limit of 60.Although the results of the k-center algorithm are obvious,because of its random selection of the initial center point,clustering results are uncontrollable,the distribution of the results of each cluster is not very close to each credit rating.In this paper,through the improvement of the traditional k-center algorithm,the k data with the largest number of records are selected as the central point,while passing the limit on the number of iterations,and then clustering,which can improve the degree of closeness to improve the efficiency of managing the credit rating of consumers,so as to observe efficiently the behavior of consumers with different credit levels,develop a corresponding credit equivalent discount strategy to improve business interests.At the end of this paper,the paper summarizes the work,analyzes the advantages and disadvantages of the algorithm,and prospects for the future work.