Research on Spatio-temporal Association Rules Mining Algorithm Based on FP-tree

ZHANG Xue-ping; LI Wei-cheng; ZHU Yu-hua

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Research on Spatio-temporal Association Rules Mining Algorithm Based on FP-tree

更新时间：2026-09-04

- Research on Spatio-temporal Association Rules Mining Algorithm Based on FP-tree
- Vol. 33, Issue 8, Pages: 130-133(2016)
- 作者机构：
  
  河南工业大学机构信息科学与工程学院,河南,郑州,450001
- 作者简介：
- 基金信息：
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- Published：2016
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张雪萍, 李围成, 祝玉华. 基于FP-树的时空关联规则挖掘算法研究[J]. 微电子学与计算机, 2016, 33(8): 130-133, 138.

ZHANG Xue-ping, LI Wei-cheng, ZHU Yu-hua. Research on Spatio-temporal Association Rules Mining Algorithm Based on FP-tree[J]. 2016, 33(8): 130-133.
张雪萍, 李围成, 祝玉华. 基于FP-树的时空关联规则挖掘算法研究[J]. 微电子学与计算机, 2016, 33(8): 130-133, 138. DOI：

ZHANG Xue-ping, LI Wei-cheng, ZHU Yu-hua. Research on Spatio-temporal Association Rules Mining Algorithm Based on FP-tree[J]. 2016, 33(8): 130-133. DOI：

摘要

提出一种基于FP-树的时空关联规则挖掘算法

即STFP-tree(Spatio-temporal FP-tree)算法.算法对时空数据进行时间上的划分

并用空间谓词分析空间对象间的位置关系

最终生成同时考虑时间和空间特征的FP-树

进而挖掘出强时空关联规则.实验结果证明了算法的有效性

适用于对时空数据进行分析挖掘.

Abstract

A spatio-temporal association rules mining algorithm based on FP-tree(STFP-tree) is proposed. It divides the data into time

and uses spatial predicates to analyze the position relationship between the spatial objects.Finally

it generates a FP-tree

which considers spatio-temporal features

and then gets strong spatio-temporal association rules. Experiments demonstrate that the algorithm can be well performed.It is suitable for the analysis and mining of spatio-temporal data.

关键词

Keywords

references

Xiaoxi Du, Ruoming Jin, Liang Ding Victor, et al. Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets[C]//Proc of the 15th ACM SIGKDD Int Conf on Knowledge and Data Mining. New York: ACM, 2009: 1135-1144.

Jiawei Han, Jian Pei, Yiwen Yin. Mining frequent patterns without candidate generation[C]//Proc of the 2000 ACM-SIGMOD Int'l Conf on Management of Data. Dallas, TX: ACM Press, 2000: 1-12.

TAN Pang-ning, STEINBACH Michael, KUMAR Vipin. 数据挖掘导论[M]. 范明, 范宏建, 译. 北京: 人民邮电出版社, 2006.

Rakesh Agrawal, Ramakrish Srikant. Fast algorithms for mining association rules[C]//Proc of 1994 Int'l Conf on Very Large Data Bases. San-tiago, Chili: VLDB Endowment, 1994: 487-499.

Krzysztof Koperski, Jiawei Han. Discovery of spatial association rules in geographic information databases[C]//prceedings of the 4th International Symposium. Portland, UE, USA: IEEE, 1995: 47-66

Hui Yang, Srinivasan Parthasarathy. Mining spatial and spatio-temporal patterns in scientific data[C]//The 22nd International Conference on Data Engineering. Atlanta: IEEE computer society, 2006.

Hamed C, Le G. An online spatio-temporal association rule mining framework for analyzing and estimating sensor data[C]//Proc of the 2009 Int Database Engineering % Applications Symp. New York: ACM, 2009: 217-226.

Verhein Florian, Chawla Sanjay. Mining spatio-temporal patterns in object mobility databases[J]. Data Mining and Knowledge Discovery, 2008, 16(1): 5-38. DOI: 5

夏英, 张俊, 王国胤. 时空关联规则挖掘算法及其在ITS中的应用[J]. 计算机科学, 2011, 38(9): 173-176. DOI: 173

Kelvin Leong, Junco Li, Stephen Chan, et al. An Application of the Dynamic Pattern Analysis Framework to the Analysis of Spatial-Temporal Crime Relationships[J]. Journal of Universal Computer Scitence, 2009, 15(9): 1852-1870. DOI: 1852

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. DOI: 455

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