Efficient algorithms for mining up-to-date high-utility patterns |
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| Affiliation: | 1. Innovative Information Industry Research Center (IIIRC), School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town Xili, Shenzhen 518055, PR China;2. Shenzhen Key Laboratory of Internet Information Collaboration, School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town Xili, Shenzhen 518055, PR China;3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC;4. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC;5. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC;1. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan;2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan;3. Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan;4. Department of Information Management, National University of Kaohsiung, Kaohsiung 811, Taiwan;1. Department of Computer Engineering, Sejong University, Seoul, Republic of Korea;2. Department of Computer Science, Chungbuk National University, Cheongju, Republic of Korea;1. Department of Computer Engineering, Kyung Hee University, South Korea;2. College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia;1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China;2. School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China;3. Department of Computer Science, University of Nevada, Las Vegas, USA;4. Department of Telecommunications, VSB-Technical University of Ostrava, Czech Republic |
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| Abstract: | High-utility pattern mining (HUPM) is an emerging topic in recent years instead of association-rule mining to discover more interesting and useful information for decision making. Many algorithms have been developed to find high-utility patterns (HUPs) from quantitative databases without considering timestamp of patterns, especially in recent intervals. A pattern may not be a HUP in an entire database but may be a HUP in recent intervals. In this paper, a new concept namely up-to-date high-utility pattern (UDHUP) is designed. It considers not only utility measure but also timestamp factor to discover the recent HUPs. The UDHUP-apriori is first proposed to mine UDHUPs in a level-wise way. Since UDHUP-apriori uses Apriori-like approach to recursively derive UDHUPs, a second UDHUP-list algorithm is then presented to efficiently discover UDHUPs based on the developed UDU-list structures and a pruning strategy without candidate generation, thus speeding up the mining process. A flexible minimum-length strategy with two specific lifetimes is also designed to find more efficient UDHUPs based on a users’ specification. Experiments are conducted to evaluate the performance of the proposed two algorithms in terms of execution time, memory consumption, and number of generated UDHUPs in several real-world and synthetic datasets. |
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| Keywords: | Data mining Utility mining Up-to-date high-utility patterns Level-wise UDU-list structures |
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