An Analysis Of The Online Shopping Behavior Based On Data Mining

With the rapid development of the network, the online shopping hinging on the network grows fast in the frame of national and global.as a new type of consumption patterns. More and more people get more attention on the Online shopping industry.At the same time, different methods of research and data analysis has also been applied on every aspect of the online shopping. Data mining as a new data analysis methode can be used in online shopping analysis to extraet the discipline, forecast trend, and diseover patterns. It is very beneficial for promoting the online shopping industry to develop healthy and orderly.The research analysis the three elements of the online shopping behavior—behavior process, behavior result and behavior corpus, by methods of data mining with the tool SPSS Clementine 11.1, on the basis of the online shopping data collected from the users of clap nets throungh the questionnaire.In the research, we explored that if the seller's credit and the search results ranking can influence the purchase decisions of the online shopping users through analysising the online shopping behavior process of them by association rules analysis, discussed the behavior results to find their buys habits and features between different online shopping groups by clustering anylysis, use the C5.0 algorithm and Quest algorithm to build the forecast model of potential customer of online shopping through researching the behavior corpus of online shopping by the classification and prediction analysis method.The conclusion of this research can provides the refrence for the site management to the operators of E-commerce site representatived by clap nets, and online stores operation to bussinessman. And the conclusion of this research can provides support for formulation on network marketing strategy to online shopkeepers,Under the influence of the objective conditions, this work has some limitations, such as limitation on data sample, data quality should being enhanced further, and data being used enoughly. These problems need to be improved.This research is focused on the application. It combines the requirement to set the analysis task and objeetive. The analysis results has praetical value.