Unveiling Valuable Insights: Leveraging Apriori Algorithm for Association Rule Mining and Analysis

In the era of big data, it is particularly important to use big data technology to extract valuable information from large datasets, and the significance of data mining lies in exploring the hidden knowledge within the data. This paper focuses on data mining on movie datasets and uses association rule techniques to extract correlation relationships among valuable data items from the dataset. The Apriori algorithm is a classic algorithm for association rules, which identifies frequent item sets to generate strong association rules and ultimately reveal the relationships between different datasets. Through the validation using datasets and the implementation of the Apriori algorithm in Python, this paper demonstrates the effectiveness of revealing the strongest association relationships within the data. By discovering association rules within the dataset, it helps us understand the hidden patterns and correlations behind the data, providing references and support for decision-making and predictions. On the basis of the prior algorithm of association rules, the data correlation analysis shows that users can recommend movies more accurately according to the strong correlation relationship between movie types, thus providing reference significance for various practical applications based on association rules.