A Novel Association Rule Mining Method for Streaming Temporal Data

作     者：Zheng, Hui Li, Peng He, Jing 

作者机构：School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Nanjing China School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia 

出 版 物：《Annals of Data Science》 (Ann. Data Sci.)

年 卷 期：2022年第9卷第4期

页      面：863-883页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is supported in part by the National Key R & D Program of China (No. 2018YFB1003201)  the Natural Science Foundation of P. R. China (No. 61672296  No. 61602261  No. 61572260  No. 61872196  No. 61332013  No. 61872194  No. 61902196)  Scientific and Technological Support Project of Jiangsu Province (No. BE2017166  and No. BE2019740)  Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province (No. 18KJA520008)  Six Talent Peaks Project of Jiangsu Province (RJFW-111)  the ARC Discovery Early Career Research Award (No. DE130100911)  the ARC Discovery Project (No. DP130101327)  the ARC Linkage Project (No. LP100200682)  the International Science and Technology Cooperation Projects (No. 2016D10008  No. 2013DFG12810  No. 2013C24027)  the Municipal Natural Science Foundation of Ningbo (No. 2015A610119)  the Natural Science Foundation of Zhejiang Province (No. Y16F020002)  the Guangzhou Science and Technology Project (No. 2016201604030034)  and NUPTSF (No. NY220014 and No. NY220188) 

主　　题：Association rule mining Streaming data Potential relationships Timestamp Value Data mining Association rule learning 

摘      要：Streaming temporal data contains time stamps and values, challenging to quantify relationships of time stamps and corresponding values. Moreover, the characteristics and relationships of streaming temporal data are not invariable. Thus, it is impossible to analyse all data by a trained model at the beginning of data streams. Practically, the trained model to analyse streaming temporal data should change according to the increasing volume of data. Association rule mining, on the other hand, can find potential relationships from given data. This paper proposes an association rule mining method for streaming temporal data to discover potential relationships from streaming temporal data. Our experiments verify our proposed method. A public data set is applied to compare the performance of the proposed method and its counterpart. A small data set is also applied for two case studies to further illustrate our proposed method mine association rules with streaming temporal data with time stamps and corresponding values. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.