Rule mining has emerged as a crucial technique in data mining and knowledge discovery, enabling the extraction of valuable insights and patterns from vast datasets. This has garnered significant attention from both academic and industrial communities. However, there is a lack of bibliometric and visualization research on rule mining, leading to an unclear delineation of research topics and trends in the field. To fill this gap, this paper provides a comprehensive and up-to-date bibliometric analysis of rule mining, covering 4524 publications published between 1987 and 2022. Using various metrics and visualization techniques, we examine the patterns, trends, and evolution of rule mining. The results show a sustained growth in rule mining research, with a significant increase in publication output in recent years, and its rapid expansion into new areas such as explainable artificial intelligence and privacy protection. While the majority of publications come from Asia, the National Natural Science Foundation of China emerges as the top funding agency in the field. We also identify highly productive authors and significant members of co-authorship networks, as well as the most influential publications and citation bursts. The need for international collaboration and the integration of diverse research perspectives is highlighted. Despite the progress in rule mining, several challenges still require further research, including scalability and efficiency, explainability, network security and privacy protection, and personalized and user-centered design. Overall, this paper provides a valuable roadmap for researchers, policymakers, and practitioners interested in rule-mining research.

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