Analysis Of Data Stream Association Rules Based On Large Scale Social Survey

With the wide application of data mining technology,data storage and processing technology has also developed rapidly.It is still a subject worthy of study to extract the valuable information contained in the mass flow data,create value for the society.Stream association rule mining is based on stream data,the focus of the research is to establish the summary data and update the established algorithm model in time to achieve the purpose of acquiring association rules quickly.In this paper,we apply the stream association rule algorithm to large-scale social survey data,construct family portrait,and build correlation modeling for household durable goods consumption problem in our country.We systematically summarize the relevant theoretical knowledge of flow data,data mining and association rules,analyze the challenges and the application of stream association rules we are facing in the current and future under the background of big data.We implement and apply the FP-stream algorithm.In the context of large-scale social survey,we construct two scenarios,namely "traffic consumption correlation" and "consumer consumption and durable goods consumption type correlation",and give some suggestions on the consumption of durable goods according to the results of the analysis.