Research On Association Rules Algorithm And Forecasting The Stock Data

Association Rules is an important subject in the research field of Data Mining, often used in the retail, telecommunications, financial industry, the insurance industry and medical services, and other fields. This paper studies the association rules algorithm, including Apriori and some improved algorithm, focusing on the shortcomings of the Mining Association Rules algorithm in the support-confidence framework, using Heckerman-certain factor to enhance the conditions of rules, at the same timein-depth study the characteristics of the rules, gives a new definition of the rules, improves Apriori algorithm, finally take the improved algorithm in the application of analysis and forecasting stock data, the introduction of the new stock data preprocessing methods, results shows that the improved algorithm is effective.