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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