Mining inter-transaction association rules from multiple time-series data

机译：从多个时间序列数据中挖掘交易交互关联规则

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Association rule mining is one of the most widely used methods for discovering interesting relations between variables. Time series as a common sequence data have some unique character, such as pervasively connected, endless and time-related. Therefore research on multivariate time series data mining is a hot spot in data mining. This paper first compresses the continuous time series. Then in order to make the mining rules reflect the characteristics of multivariate time series data, our paper designs a new algorithm called IAMTL, which can mine the rules from the fix time span. For the reason that time series data have the characteristic of continuity, so an increment version of IATML is provided. At last, we use prerequisite and the consequent windows to verify the correctness of the rules.

机译：关联规则挖掘是发现变量之间有趣关系的最广泛使用的方法之一。时间序列作为通用序列数据具有一些独特的特征，如普遍联系，无穷无尽和与时间有关。因此，多元时间序列数据挖掘的研究成为数据挖掘的热点。本文首先压缩连续时间序列。然后，为了使挖掘规则能够反映多元时间序列数据的特征，本文设计了一种新算法IAMTL，该算法可以从固定时间跨度中挖掘规则。由于时间序列数据具有连续性的特点，因此提供了IATML的增量版本。最后，我们使用前提条件和相应的窗口来验证规则的正确性。

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来源
《2017 International Conference on Security, Pattern Analysis, and Cybernetics》|2017年|158-163|共6页
会议地点 Shenzhen(CN)
作者
Chunkai Zhang; Xudong Zhang; Zoe L Jiang; Qing Liao; Lin Yao; Xuan Wang;
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School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, 518055, People's Republic of China;

School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, 518055, People's Republic of China;

School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, 518055, People's Republic of China;

School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, 518055, People's Republic of China;

School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, 518055, People's Republic of China;

School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, 518055, People's Republic of China;

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正文语种 eng
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关键词
Time series analysis; Data mining; Heuristic algorithms; Security; Pattern analysis; Cybernetics; Itemsets;

机译：时间序列分析;数据挖掘;启发式算法;安全性;模式分析; Cybernetics;项目集;;

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专利

1. A Novel Approach for Inter-transaction Association Rule Mining [J] . S.Nandagopal, V.P.Arunachalam, S.Karthik Journal of applied sciences research . 2012,第7期

机译：交易间关联规则挖掘的新方法
2. From intra-transaction to generalized inter-transaction: Landscaping multidimensional contexts in association rule mining [J] . Li Q, Feng L, Wong A Information Sciences: An International Journal . 2005,第3a4期

机译：从交易内到广义交易：在关联规则挖掘中对多维上下文进行美化
3. Meta-association rules for mining interesting associations in multiple datasets [J] . Ruiz M. D., Gomez-Romero J., Molina-Solana M., Applied Soft Computing . 2016,第Null期

机译：元关联规则，用于在多个数据集中挖掘有趣的关联
4. Mining inter-transaction association rules from multiple time-series data [C] . Chunkai Zhang, Xudong Zhang, Zoe L Jiang, International Conference on Security, Pattern Analysis, and Cybernetics . 2017

机译：从多个时间序列数据挖掘交易间关联规则
5. Association rule mining over multiple databases: Partitioned and incremental approaches. [D] . Kona, Hima Valli. 2003

机译：在多个数据库上进行关联规则挖掘：分区和增量方法。
6. Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases [O] . Marcos Martínez-Romero, Martin J OConnor, Attila L Egyedi, 2019

机译：使用关联规则挖掘和本体从多个生物医学数据库生成元数据推荐
7. From Intra-transaction to Generalized Inter-transaction: Landscaping Multidimensional Contexts in Association Rule Mining [O] . Li Q, Feng L., Wong A.K.Y. 2005

机译：从交易内到广义交易：在关联规则挖掘中对多维上下文进行美化

Mining inter-transaction association rules from multiple time-series data

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