Abstract
Utility-pattern mining in data has received a lot of attention from the knowledge discovery in database (KDD) community due to its high potential for many applications such as finance, biomedicine, manufacturing, e-commerce, and social media. Current research in utility-pattern mining primarily focuses on discovering patterns of high value (e.g., high profit) in large databases and analyzing/learning important factors (e.g., economic factors) in a data mining process. One of the most popular applications of utility mining is the analysis of large transactional databases to discover high-utility itemsets, which are sets of items that yield a high profit when purchased together.
Highlights
Utility-pattern mining in data has received a lot of attention from the knowledge discovery in database (KDD) community due to its high potential for many applications such as finance, biomedicine, manufacturing, e-commerce, and social media
Current research in utility-pattern mining primarily focuses on discovering patterns of high value in large databases and analyzing/learning important factors in a data mining process
In the article ‘‘An effective approach for the diverse group stock portfolio optimization using grouping genetic algorithm,’’ by Chen et al, the authors develop an algorithm for dealing with diverse group stock portfolio optimization (DGSPO)
Summary
Utility-pattern mining in data has received a lot of attention from the knowledge discovery in database (KDD) community due to its high potential for many applications such as finance, biomedicine, manufacturing, e-commerce, and social media. SPECIAL SECTION ON UTILITY PATTERN MINING: THEORETICAL ANALYTICS AND APPLICATIONS IEEE ACCESS SPECIAL SECTION EDITORIAL: UTILITY-PATTERN MINING: THEORETICAL ANALYTICS AND APPLICATIONS
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
