A Context-Based Association Rules Mining Method for Multiple Event Sequences

HE Zhanjun; DENG Min; CAI Jiannan; LIU Qiliang

doi:10.13203/j.whugis20150616

Volume 43 Issue 5

May 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(5): 766-772. > DOI: 10.13203/j.whugis20150616

HE Zhanjun, DENG Min, CAI Jiannan, LIU Qiliang. A Context-Based Association Rules Mining Method for Multiple Event Sequences[J]. Geomatics and Information Science of Wuhan University, 2018, 43(5): 766-772. DOI: 10.13203/j.whugis20150616

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A Context-Based Association Rules Mining Method for Multiple Event Sequences

1.
Department of Geo-informatics, Central South University, Changsha 410083, China

Funds:

The Hunan Provincial Science Fund for Distinguished Young Scholars 14JJ1007

The National Natural Science Foundation of China 41471385

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Author Bio:
HE Zhanjun, PhD candidate, specializes in spatial-temporal association patterns mining and applications. E-mail:hezhanjun000@126.com
Corresponding author:
DENG Min, PhD, professor. E-mail:dengmin208@tom.com
Received Date: June 07, 2016
Published Date: May 04, 2018

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Abstract

Abstract

Association rules mining of event sequences aims to discover interesting patterns of different neighboring events and plays an important role in understanding their mutual relationship. However, for most existing methods, the distribution characters of events in the sequences are usually ignored and selecting proper thresholds is really a tough task, which brings about the problems of redundant results or interesting rules missing. Thus, new measuring indexes were defined and a context-based method for multiple event sequences mining was proposed. Results of both the simulated experiment and practical cases emphasized that the proposed method could effectively reduce the redundancy in the results in comparison with the classic MOWCATL method. Moreover, there was good consistency between the measuring indexes, which eases the selection of generated rules. Finally, the proposed method was applied to mine association rules between and PM_2.5 concentration and several meteorological factors. Results indicated that the most associated meteorological factor with PM_2.5 concentration was the humidity and an eligible environment for high PM_2.5 concentration were high humidity, low temperature and weak winds.
- context,
- data mining,
- association rules,
- multiple event sequences,
- PM_2.5

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[1]	Mennis J, Liu J W. Mining Association Rules in Spatio-Temporal Data:An Analysis of Urban Socioeconomic and Land Cover Change[J]. Transactions in GIS, 2005, 9(1):5-17 doi: 10.1111/tgis.2005.9.issue-1
[2]	沙宗尧, 李晓雷.异质环境下的空间关联规则挖掘[J].武汉大学学报·信息科学版, 2009, 34(12):1480-1484 http://ch.whu.edu.cn/CN/abstract/abstract1471.shtml Sha Zongyao, Li Xiaolei. Algorithm of Mining Spatial Association Data under Spatially Heterogeneous Environment[J]. Geomatics and Information Science of Wuhan University, 2009, 34(12):1480-1484 http://ch.whu.edu.cn/CN/abstract/abstract1471.shtml
[3]	柴思跃, 苏奋振, 周成虎.基于周期表的时空关联规则挖掘方法与实验[J].地球信息科学学报, 2011, 13(4):455-464 http://www.dqxxkx.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=24600 Cai Siyue, Sui Fenzhen, Zhou Chenghu. Period Table Based Spatio-Temporal Association Rules Mining[J]. Journal of Geo-Information Science, 2011, 13(4):455-464 http://www.dqxxkx.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=24600
[4]	陈江平, 黄炳坚.数据空间自相关性对关联规则的挖掘与实验分析[J].地球信息科学学报, 2011, 13(1):109-117 http://www.dqxxkx.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=23534 Chen Jiangping, Huang Bingjian. Application and Effects of Data Spatial Autocorrelation on Association Rule Mining[J].Journal of Geo-Information Science, 2011, 13(1):109-117 http://www.dqxxkx.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=23534
[5]	Feng L, Dillon T, Liu J. Inter-transactionalAssociation Rules for Multi-Dimensional Contexts for Prediction and Their Application to Studying Meteorological Data[J]. Data and Knowledge Engineering, 2001, 37(1):85-115 doi: 10.1016/S0169-023X(01)00003-9
[6]	Agrawal R, Imieliński T, Swami A. Mining Association Rules Between Sets of Items in Large Databases[C]. ACM SIGMOD International Conference on Management of Data, Washington D C, 1993 https://dl.acm.org/citation.cfm?doid=170035.170072
[7]	Agrawal R, Srikant R. MiningSequential Patterns[C]. The 6th International Conference on Data Engineering, Taipei, China, 1995 http://dl.acm.org/citation.cfm?id=655281
[8]	Srikant R, Agrawal R. MiningSequential Patterns:Generalizations and performance improvements[M]. New York:Springer, 1996
[9]	Zaki M J. SPADE:An Efficient Algorithm for Mining Frequent Sequences[J]. Machine Learning, 2001, 42(1/2):31-60 doi: 10.1023/A:1007652502315
[10]	Pei J, Han J, Mortazavi-Asl B, et al. Prefixspan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth[C]. The 20th International Council for Open and Distance Education, Dusseldorf, Germany, 2001 https://www.computer.org/csdl/proceedings/icde/2001/1001/00/10010215-abs.html
[11]	Mannila H, Toivonen H, Verkamo A I. Discovery ofFrequent Episodes in Event Sequences[J]. Data Mining and Knowledge Discovery, 1997, 1(3):259-289 doi: 10.1023/A:1009748302351
[12]	Harms S K, Deogun J, Saquer J, et al. Discovering Representative Episodal Association Rules from Event Sequences Using Frequent Closed Episode Sets and Event Constraints[C]. IEEE International Conference on Data Mining, San Jose, California, USA, 2001 http://dl.acm.org/citation.cfm?id=657879
[13]	Harms S K, Deogun J, Tadesse T. Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences[M]. New York:Springer, 2002
[14]	Tadesse T, Wilhite D A, Harms S K, et al. DroughtMonitoring Using Data Mining Techniques:A Case Study for Nebraska, USA[J]. Natural Hazards, 2004, 33(1):137-159 doi: 10.1023/B:NHAZ.0000035020.76733.0b
[15]	石岩, 邓敏, 刘启亮, 等.海陆气候事件关联规则挖掘方法[J].地球信息科学学报, 2014, 16(2):182-190 http://www.dqxxkx.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=24909 Shi Yan, Deng Min, Liu Qiliang, et al. Discovering Sequential Association Rules Between SingleOcean Climate Index and Land Abnormal Climate Events[J]. Journal of Geo-Information Science, 2014, 16(2):182-190 http://www.dqxxkx.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=24909
[16]	Zhang X W, Su F Z, Shi Y, et al. Association Rule Mining Based on Spatio-Temporal Processes of Spatial Distribution Patterns[C]. The 4th International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, 2008 http://www.researchgate.net/publication/269318324_Association_rule_mining_based_on_spatio-temporal_processes_of_spatial_distribution_patterns
[17]	Yoo J S, Bow M. Mining Spatial Colocation Patterns:A Different Framework[J]. Data Mining and Knowledge Discovery, 2012, 24(1):159-194 doi: 10.1007/s10618-011-0223-0
[18]	Qian F, He Q, Chiew K, et al. Spatial Co-location Pattern Discovery Without Thresholds[J]. Knowledge and Information Systems, 2012, 33(2):419-445 doi: 10.1007/s10115-012-0506-9
[19]	Whitby K T. ThePhysical Characteristics of Sulfur Aerosols[J]. Atmospheric Environment, 1978, 12(1-3):135-159 doi: 10.1016/0004-6981(78)90196-8
[20]	Huang X, He L, Hu M, et al. AnnualVariation of Particulate Organic Compounds in PM2.5 in the Urban Atmosphere of Beijing[J]. Atmospheric Environment, 2006, 40(14):2449-2458 https://www.sciencedirect.com/science/article/pii/S1352231006000033
[21]	Song Y, Tang X, Xie S, et al. SourceApportionment of PM2.5 in Beijing in 2004[J]. Journal of Hazardous Materials, 2007, 146(1/2):124-130 http://www.doc88.com/p-581687597795.html

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