Research on teaching quality evaluation system based on data mining technology

Abstract

The task of quickly finding target information from a huge database is required in the era of big data. Association rule algorithms, which mainly carry out research in terms of dimensionality of data, abstraction level and types of processing variables, have become an effective method for solving data mining problems. Data mining techniques can discover a lot of information hidden behind a large amount of data and provide effective data support for important decisions. Association rule-based data mining techniques are one of the most important research areas, and the use of association rule algorithms can identify the key factors affecting the research object. In this paper, the classical association rule algorithm Apriori and its improvement algorithm are studied to investigate the factors influencing teaching quality in vocational institutions using data mining techniques. The article combines the association rule algorithm of data mining technology and database technology, and constructs a database matching the association rule mining through the process of data collection and data pre-processing. The key factors of teaching quality are analyzed by using association rule algorithm, and the teaching quality evaluation system based on data mining technology is studied in depth.