Frequent itemset mining is one of the core tasks in the field of data mining,aiming to discover patterns that frequently occur in a database.These patterns play a crucial role in various data mining tasks such as association rule discovery,classification,and anomaly detection and so on.As the size of the itemset increases,the number of combinations of itemset increases exponentially,leading to a sharp increase in computational complexity.Researchers have been working hard to develop efficient algorithms to solve this problem.This study focuses on algorithms,compact representations,and cutting-edge applications for frequent itemset mining,exploring the working principles,advantages,and limitations of different technologies in depth,in order to comprehensively summarize the research status in this field.Finally,this study further discusses the frontier development trend in this field,and points out the future potential research directions,such as computational efficiency,constraint-based frequent itemset mining,interpretability of patterns and innovative applications of algorithms in different fields.
维普
万方数据
