Optimization Design of Apriori Algorithm Based on Big Data Analysis and Cloud Computing

With the development of the era of big data, data information is used more and more in life, and the application of data analysis is more and more extensive. The algorithm of data mining and association analysis are to find some internal relations in a large number of data sets. Although big data has brought abundant information and knowledge, its complexity in scale, rapid growth, diversity in forms and low value density have also brought severe challenges to traditional data processing technology. Therefore, there is an urgent need for big data processing technology to adapt to the big data environment. Based on the analysis of association rules and the principles of the Apriori algorithm, this article proposes an improved algorithm for improving the performance of the Apriori algorithm in the case of large data volumes, in order to address the drawbacks of the system's I/O load and CPU computational pressure caused by the large number of transactions when the Apriori algorithm scans the database. Using the Apriori algorithm to study the association rules of these data, in order to conduct reasonable analysis of the correlation between the data. Thus, the product interest set corresponding to the user's personality can be excavated. Realize the accuracy of individual product recommendations and bundled product recommendations, enhance the influence of the client, stimulate consumer purchasing desire, and achieve maximum profit.