Mining High Utility Itemsets over Uncertain Databases

• Published in: 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery pp. 235 - 238
• Main Authors: Yuqing Lan, Yang Wang, Yanni Wang, Shengwei Yi, Dan Yu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2015
• Subjects: Accidents; Algorithm design and analysis; Data mining; Itemsets; Memory management; Yttrium
• DOI: 10.1109/CyberC.2015.76

Recently, with the growing popularity of Internet of Things (IoT) and pervasive computing, a large amount of uncertain data, i.e. RFID data, sensor data, real-time monitoring data, etc., has been collected. As one of the most fundamental issues of uncertain data mining, the problem of mining uncertain frequent item sets has attracted much attention in the database and data mining communities. Although some efficient approaches of mining uncertain frequent item sets have been proposed, most of them only consider each item in one transaction as a random variable and ignore the utility of each item in the real scenarios. In this paper, we focus on the problem of mining high utility item sets (MHUI) over uncertain databases, in which each item has a utility. In order to solve the MHUI problem over uncertain databases, we propose an efficient mining algorithm, named UHUI-apriori. Extensive experiments on both real and synthetic datasets verify the effectiveness and efficiency of our proposed solutions.