Efficient algorithms for mining up-to-date high-utility patterns

Jerry Chun Wei Lin*, Wensheng Gan, Tzung Pei Hong, Vincent Shin-Mu Tseng

*此作品的通信作者

研究成果: Article同行評審

52 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號612
頁(從 - 到)648-661
頁數14
期刊Advanced Engineering Informatics
29
發行號3
DOIs
出版狀態Published - 1 八月 2015

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