Abstract
One of various pattern mining techniques, the High Utility Pattern Mining (HUPM) is a method for finding meaningful patterns from non-binary databases by considering the characteristics of the items. Recently, new data continues to flow over time in diverse fields such as sales data of market, heartbeat sensor data, and social network service. Since these data have a feature that recently generated data have higher influence than the old data, research has been focused on how to efficiently extract hidden knowledge from time-sensitive databases. In this paper, we propose indexed list-based algorithm that mines recent high utility pattern considering the arrival time of inserted data in an environment where new data is continuously accumulated. In other words, to treat the importance of recent data higher than the that of old data, our algorithms reduces the utility values of old transactions according to the time the data is inserted by applying damped window model concept. Moreover, we carry out various experiments to compare our method with state-of-the-art algorithms using real and synthetic datasets in diverse circumstances. Experimental results show that our algorithm outperforms competitors in terms of execution time, memory usage, and scalability test.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.