Efficient algorithms for mining up-to-date high-utility patterns. Issue 3 (August 2015)

• Record Type: Journal Article
• Authors: Lin, Jerry Chun-Wei; Gan, Wensheng; Hong, Tzung-Pei; Tseng, Vincent S.
• Is Part Of: Advanced engineering informatics. Volume 29:Issue 3(2015:Aug.)
• Page Start: 648
• Page End: 661
• Publication Date: 2015-08
• Subjects: Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2015.06.002
• Languages: English
• ISSNs: 1474-0346

Abstract

<abstract xml:lang="en" abstract-type="author" id="ab005"> <title id="st150">Abstract</title> <sec> <p id="sp005">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.</p> </sec> </abstract>