·510· 智能系统学报 第16卷 gorithm[J].Review of bioinformatics and biometrics, MA Hui,TANG Yong,PAN Yan.A FP-tree based parti- 2013,2(2):29-36 tion mining approach to discovering temporal association [16]赵益.多时间序列上时序关联规则的挖掘D].上海：东 rules[J].Computer engineering,2006,32(17):132-134. 华大学，2018. [23]张建业，潘泉，张鹏等.基于斜率表示的时间序列相似 ZHAO Yi.Discovery of tempopal assocition rules in mul- 性度量方法[J].模式识别与人工智能，2007,20(2)： tivariate time series[D],Shang Hai:Donghua University, 271-274. 2018. ZHANG Jianye,PAN Quan,ZHANG Peng,et al.Simil- [17]CHEN Yicheng,PENG W C,LEE S Y.CEMiner-An arity measuring method in time series based on slope[J]. efficient algorithm for mining closed patterns from time Pattern recognition and artificial intelligence,2007,20(2) interval-based data[C]//Proceedings of the IEEE 11th In- 271-274. ternational Conference on Data Mining.Vancouver, [24]SALEM M Z.Effects of perfume packaging on Basque Canada,2011:121-130 female consumers purchase decision in Spain[J].Manage- [18]RUAN Guangchen,ZHANG Hui,PLALE B.Parallel and ment decision,.2018,56(8):1748-1768. quantitative sequential pattern mining for large-scale in- [25]LI Hailin,WU Y J,CHEN Yewang.Time is money:dy- terval-based temporal data[Cl//Proceedings of 2014 IEEE namic-model-based time series data-mining for correla- International Conference on Big Data (Big Data).Wash- tion analysis of commodity sales[J].Journal of computa- ington,USA,2014:32-39. tional and applied mathematics,2020,370:112659 [19]SCHLUTER T,CONRAD S.Mining several kinds of 作者简介： temporal association rules enhanced by tree structures[C/ 李海林，教授，博士生导师，主要 Proceedings of the 2nd International Conference on In- 研究方向为数据挖掘与决策支持。主 formation,Process,and Knowledge Management.Saint 持国家自然科学基金项目2项、省部 级基金项目4项。发表学术论文 Maarten.Netherland Antilles.2010:86-93. 60余篇。 [20]RASHID MM.GONDAL I.KAMRUZZAMAN J.Min- ing associated patterns from wireless sensor networks[J]. IEEE transactions on computers,2015,64(7):1998-2011. [21]PANKAJ G,SAGAR BB.Discovering weighted calen- 龙芳菊，硕士研究生，主要研究方 向为数据挖掘与企业管理。 dar-based temporal relationship rules using frequent pat- tern tree[J].Indian journal of science and technology, 2016,9(28:1-6. [22]马慧，汤痛，潘炎.一种基于P-树的时态关联规则的分 区挖掘方法[J.计算机工程，2006,32(17)：132-134 [责任编辑：李雪莲]gorithm[J]. Review of bioinformatics and biometrics, 2013, 2(2): 29−36. 赵益. 多时间序列上时序关联规则的挖掘 [D]. 上海: 东 华大学, 2018. ZHAO Yi. Discovery of tempopal assocition rules in mul￾tivariate time series[D], Shang Hai: Donghua University, 2018. [16] CHEN Yicheng, PENG W C, LEE S Y. CEMiner—An efficient algorithm for mining closed patterns from time interval-based data[C]//Proceedings of the IEEE 11th In￾ternational Conference on Data Mining. Vancouver, Canada, 2011: 121−130. [17] RUAN Guangchen, ZHANG Hui, PLALE B. Parallel and quantitative sequential pattern mining for large-scale in￾terval-based temporal data[C]//Proceedings of 2014 IEEE International Conference on Big Data (Big Data). Wash￾ington, USA, 2014: 32−39. [18] SCHLÜTER T, CONRAD S. Mining several kinds of temporal association rules enhanced by tree structures[C]// Proceedings of the 2nd International Conference on In￾formation, Process, and Knowledge Management. Saint Maarten, Netherland Antilles, 2010: 86−93. [19] RASHID M M, GONDAL I, KAMRUZZAMAN J. Min￾ing associated patterns from wireless sensor networks[J]. IEEE transactions on computers, 2015, 64(7): 1998−2011. [20] PANKAJ G, SAGAR B B. Discovering weighted calen￾dar-based temporal relationship rules using frequent pat￾tern tree[J]. Indian journal of science and technology, 2016, 9(28): 1–6. [21] 马慧, 汤庸, 潘炎. 一种基于 FP-树的时态关联规则的分 区挖掘方法 [J]. 计算机工程, 2006, 32(17): 132−134. [22] MA Hui, TANG Yong, PAN Yan. A FP-tree based parti￾tion mining approach to discovering temporal association rules[J]. Computer engineering, 2006, 32(17): 132−134. 张建业, 潘泉, 张鹏等. 基于斜率表示的时间序列相似 性度量方法 [J]. 模式识别与人工智能, 2007, 20(2): 271−274. ZHANG Jianye, PAN Quan, ZHANG Peng, et al. Simil￾arity measuring method in time series based on slope[J]. Pattern recognition and artificial intelligence, 2007, 20(2): 271−274. [23] SALEM M Z. Effects of perfume packaging on Basque female consumers purchase decision in Spain[J]. Manage￾ment decision, 2018, 56(8): 1748−1768. [24] LI Hailin, WU Y J, CHEN Yewang. Time is money: dy￾namic-model-based time series data-mining for correla￾tion analysis of commodity sales[J]. Journal of computa￾tional and applied mathematics, 2020, 370: 112659. [25] 作者简介： 李海林，教授，博士生导师，主要 研究方向为数据挖掘与决策支持。主 持国家自然科学基金项目 2 项、省部 级基金项目 4 项。发表学术论文 60 余篇。 龙芳菊，硕士研究生，主要研究方 向为数据挖掘与企业管理。 [ 责任编辑：李雪莲 ] ·510· 智 能 系 统 学 报 第 16 卷