A Mining Algorithm for Grid Customer Information Based on Big Data

With the continuous advancement of information management system construction, power informatization has gradually achieved office automation for power enterprises, providing decision-making and services for power enterprises, and has also shifted towards management development, while also extending to deeper levels. Despite the rapid development of artificial intelligence and big data, how to use the new and improved intelligent algorithm to explore data and improve the utilization ratio and accuracy of research data is rarely reported. This article first briefly introduces the current research and development status of power grid customer informatization, as well as the application status of customer information data mining. Then the focus was on researching data mining algorithms, which require multiple scans of the database during the data analysis process and generate a large number of candidate itemsets for optimizing classification based on these data point sets. The results show that the data mining algorithm greatly reduces time complexity; At the same time, by compressing the database size, redundant and non-standard itemsets are deleted during the calculation process, saving space. Finally, the data mining algorithm is applied to the customer information analysis model to study the imported power grid customer profile information data, in order to help decision-making and promote power grid marketing.