Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Association rule mining, a fundamental aspect of data mining, offers significant potential for optimizing knowledge engineering processes. This paper examines the implementation of association rule mining algorithms, such as Apriori and FP-Growth, within knowledge engineering systems to enhance knowledge discovery and decision support. We analyze the effectiveness of these algorithms in identifying relationships and patterns within large datasets, thereby facilitating more informed decision-making. Through case studies and empirical analysis, we illustrate how association rule mining can be leveraged to optimize knowledge management practices, providing valuable insights and improving overall system performance.

Keywords: Association Rule Mining, Knowledge Engineering, Apriori, FP Growth, Decision Support